Publications by authors named "Acharya U"

479 Publications

Functional network connectivity imprint in febrile seizures.

Sci Rep 2022 02 28;12(1):3267. Epub 2022 Feb 28.

Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bengaluru, 560029, Karnataka, India.

Complex febrile seizures (CFS), a subset of paediatric febrile seizures (FS), have been studied for their prognosis, epileptogenic potential and neurocognitive outcome. We evaluated their functional connectivity differences with simple febrile seizures (SFS) in children with recent-onset FS. Resting-state fMRI (rs-fMRI) datasets of 24 children with recently diagnosed FS (SFS-n = 11; CFS-n = 13) were analysed. Functional connectivity (FC) was estimated using time series correlation of seed region-to-whole-brain-voxels and network topology was assessed using graph theory measures. Regional connectivity differences were correlated with clinical characteristics (FDR corrected p < 0.05). CFS patients demonstrated increased FC of the bilateral middle temporal pole (MTP), and bilateral thalami when compared to SFS. Network topology study revealed increased clustering coefficient and decreased participation coefficient in basal ganglia and thalamus suggesting an inefficient-unbalanced network topology in patients with CFS. The number of seizure recurrences negatively correlated with the integration of Left Thalamus (r = - 0.58) and FC of Left MTP to 'Right Supplementary Motor and left Precentral' gyrus (r = - 0.53). The FC of Right MTP to Left Amygdala, Putamen, Parahippocampal, and Orbital Frontal Cortex (r = 0.61) and FC of Left Thalamus to left Putamen, Pallidum, Caudate, Thalamus Hippocampus and Insula (r 0.55) showed a positive correlation to the duration of the longest seizure. The findings of the current study report altered connectivity in children with CFS proportional to the seizure recurrence and duration. Regardless of the causal/consequential nature, such observations demonstrate the imprint of these disease-defining variables of febrile seizures on the developing brain.
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http://dx.doi.org/10.1038/s41598-022-07173-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8885759PMC
February 2022

Attention-based 3D CNN with residual connections for efficient ECG-based COVID-19 detection.

Comput Biol Med 2022 Feb 20;143:105335. Epub 2022 Feb 20.

Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore; Biomedical Engineering, School of Science and Technology, SUSS University, Singapore; Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan.

Background: The world has been suffering from the COVID-19 pandemic since 2019. More than 5 million people have died. Pneumonia is caused by the COVID-19 virus, which can be diagnosed using chest X-ray and computed tomography (CT) scans. COVID-19 also causes clinical and subclinical cardiovascular injury that may be detected on electrocardiography (ECG), which is easily accessible.

Method: For ECG-based COVID-19 detection, we developed a novel attention-based 3D convolutional neural network (CNN) model with residual connections (RC). In this paper, the deep learning (DL) approach was developed using 12-lead ECG printouts obtained from 250 normal subjects, 250 patients with COVID-19 and 250 with abnormal heartbeat. For binary classification, the COVID-19 and normal classes were considered; and for multiclass classification, all classes. The ECGs were preprocessed into standard ECG lead segments that were channeled into 12-dimensional volumes as input to the network model. Our developed model comprised of 19 layers with three 3D convolutional, three batch normalization, three rectified linear unit, two dropouts, two additional (for residual connections), one attention, and one fully connected layer. The RC were used to improve gradient flow through the developed network, and attention layer, to connect the second residual connection to the fully connected layer through the batch normalization layer.

Results: A publicly available dataset was used in this work. We obtained average accuracies of 99.0% and 92.0% for binary and multiclass classifications, respectively, using ten-fold cross-validation. Our proposed model is ready to be tested with a huge ECG database.
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http://dx.doi.org/10.1016/j.compbiomed.2022.105335DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8858432PMC
February 2022

Hybrid Deep Feature Generation for Appropriate Face Mask Use Detection.

Int J Environ Res Public Health 2022 02 9;19(4). Epub 2022 Feb 9.

Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore.

Mask usage is one of the most important precautions to limit the spread of COVID-19. Therefore, hygiene rules enforce the correct use of face coverings. Automated mask usage classification might be used to improve compliance monitoring. This study deals with the problem of inappropriate mask use. To address that problem, 2075 face mask usage images were collected. The individual images were labeled as either mask, no masked, or improper mask. Based on these labels, the following three cases were created: Case 1: mask versus no mask versus improper mask, Case 2: mask versus no mask + improper mask, and Case 3: mask versus no mask. This data was used to train and test a hybrid deep feature-based masked face classification model. The presented method comprises of three primary stages: (i) pre-trained ResNet101 and DenseNet201 were used as feature generators; each of these generators extracted 1000 features from an image; (ii) the most discriminative features were selected using an improved RelieF selector; and (iii) the chosen features were used to train and test a support vector machine classifier. That resulting model attained 95.95%, 97.49%, and 100.0% classification accuracy rates on Case 1, Case 2, and Case 3, respectively. Having achieved these high accuracy values indicates that the proposed model is fit for a practical trial to detect appropriate face mask use in real time.
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http://dx.doi.org/10.3390/ijerph19041939DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8871993PMC
February 2022

Artificial Intelligence Enabled Personalised Assistive Tools to Enhance Education of Children with Neurodevelopmental Disorders-A Review.

Int J Environ Res Public Health 2022 01 21;19(3). Epub 2022 Jan 21.

Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore.

Mental disorders (MDs) with onset in childhood or adolescence include neurodevelopmental disorders (NDDs) (intellectual disability and specific learning disabilities, such as dyslexia, attention deficit disorder (ADHD), and autism spectrum disorders (ASD)), as well as a broad range of mental health disorders (MHDs), including anxiety, depressive, stress-related and psychotic disorders. There is a high co-morbidity of NDDs and MHDs. Globally, there have been dramatic increases in the diagnosis of childhood-onset mental disorders, with a 2- to 3-fold rise in prevalence for several MHDs in the US over the past 20 years. Depending on the type of MD, children often grapple with social and communication deficits and difficulties adapting to changes in their environment, which can impact their ability to learn effectively. To improve outcomes for children, it is important to provide timely and effective interventions. This review summarises the range and effectiveness of AI-assisted tools, developed using machine learning models, which have been applied to address learning challenges in students with a range of NDDs. Our review summarises the evidence that AI tools can be successfully used to improve social interaction and supportive education. Based on the limitations of existing AI tools, we provide recommendations for the development of future AI tools with a focus on providing personalised learning for individuals with NDDs.
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http://dx.doi.org/10.3390/ijerph19031192DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8835076PMC
January 2022

Accurate detection of autism using Douglas-Peucker algorithm, sparse coding based feature mapping and convolutional neural network techniques with EEG signals.

Comput Biol Med 2022 Feb 9;143:105311. Epub 2022 Feb 9.

Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore; Biomedical Engineering, School of Science and Technology, SUSS University, Singapore; Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan.

Autism Spectrum Disorders (ASD) is a collection of complicated neurological disorders that first show in early childhood. Electroencephalogram (EEG) signals are widely used to record the electrical activities of the brain. Manual screening is prone to human errors, tedious, and time-consuming. Hence, a novel automated method involving the Douglas-Peucker (DP) algorithm, sparse coding-based feature mapping approach, and deep convolutional neural networks (CNNs) is employed to detect ASD using EEG recordings. Initially, the DP algorithm is used for each channel to reduce the number of samples without degradation of the EEG signal. Then, the EEG rhythms are extracted by using the wavelet transform. The EEG rhythms are coded by using the sparse representation. The matching pursuit algorithm is used for sparse coding of the EEG rhythms. The sparse coded rhythms are segmented into 8 bits length and then converted to decimal numbers. An image is formed by concatenating the histograms of the decimated rhythm signals. Extreme learning machines (ELM)-based autoencoders (AE) are employed at a data augmentation step. After data augmentation, the ASD and healthy EEG signals are classified using pre-trained deep CNN models. Our proposed method yielded an accuracy of 98.88%, the sensitivity of 100% and specificity of 96.4%, and the F1-score of 99.19% in the detection of ASD automatically. Our developed model is ready to be tested with more EEG signals before its clinical application.
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http://dx.doi.org/10.1016/j.compbiomed.2022.105311DOI Listing
February 2022

Application of photoplethysmography signals for healthcare systems: An in-depth review.

Comput Methods Programs Biomed 2022 Apr 1;216:106677. Epub 2022 Feb 1.

School of Science and Technology, Singapore University of Social Sciences, Singapore; School of Business (Information Systems), Faculty of Business, Education, Law and Arts, University of Southern Queensland, Australia; School of Engineering, Ngee Ann Polytechnic, 535 Clementi Road, 599489, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan; Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan. Electronic address:

Background And Objectives: Photoplethysmography (PPG) is a device that measures the amount of light absorbed by the blood vessel, blood, and tissues, which can, in turn, translate into various measurements such as the variation in blood flow volume, heart rate variability, blood pressure, etc. Hence, PPG signals can produce a wide variety of biological information that can be useful for the detection and diagnosis of various health problems. In this review, we are interested in the possible health disorders that can be detected using PPG signals.

Methods: We applied PRISMA guidelines to systematically search various journal databases and identified 43 PPG studies that fit the criteria of this review.

Results: Twenty-five health issues were identified from these studies that were classified into six categories: cardiac, blood pressure, sleep health, mental health, diabetes, and miscellaneous. Various routes were employed in these PPG studies to perform the diagnosis: machine learning, deep learning, and statistical routes. The studies were reviewed and summarized.

Conclusions: We identified limitations such as poor standardization of sampling frequencies and lack of publicly available PPG databases. We urge that future work should consider creating more publicly available databases so that a wide spectrum of health problems can be covered. We also want to promote the use of PPG signals as a potential precision medicine tool in both ambulatory and hospital settings.
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http://dx.doi.org/10.1016/j.cmpb.2022.106677DOI Listing
April 2022

Aleatory-aware deep uncertainty quantification for transfer learning.

Comput Biol Med 2022 Jan 24;143:105246. Epub 2022 Jan 24.

Department of ECE, Ngee Ann Polytechnic, 535 Clementi Road, 599 489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore; Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan.

The user does not have any idea about the credibility of outcomes from deep neural networks (DNN) when uncertainty quantification (UQ) is not employed. However, current Deep UQ classification models capture mostly epistemic uncertainty. Therefore, this paper aims to propose an aleatory-aware Deep UQ method for classification problems. First, we train DNNs through transfer learning and collect numeric output posteriors for all training samples instead of logical outputs. Then we determine the probability of happening a certain class from K-nearest output posteriors of the same DNN in training samples. We name this probability as opacity score, as the paper focuses on the detection of opacity on X-ray images. This score reflects the level of aleatory on the sample. When the NN is certain on the classification of the sample, the probability of happening a class becomes much higher than the probabilities of others. Probabilities for different classes become close to each other for a highly uncertain classification outcome. To capture the epistemic uncertainty, we train multiple DNNs with different random initializations, model selection, and augmentations to observe the effect of these training parameters on prediction and uncertainty. To reduce execution time, we first obtain features from the pre-trained NN. Then we apply features to the ensemble of fully connected layers to get the distribution of opacity score during the test. We also train several ResNet and DenseNet DNNs to observe the effect of model selection on prediction and uncertainty. The paper also demonstrates a patient referral framework based on the proposed uncertainty quantification. The scripts of the proposed method are available at the following link: https://github.com/dipuk0506/Aleatory-aware-UQ.
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http://dx.doi.org/10.1016/j.compbiomed.2022.105246DOI Listing
January 2022

Automated emotion recognition: Current trends and future perspectives.

Comput Methods Programs Biomed 2022 Mar 19;215:106646. Epub 2022 Jan 19.

School of Engineering, Ngee Ann Polytechnic, Clementi 599489, Singapore; Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore.

Background: Human emotions greatly affect the actions of a person. The automated emotion recognition has applications in multiple domains such as health care, e-learning, surveillance, etc. The development of computer-aided diagnosis (CAD) tools has led to the automated recognition of human emotions.

Objective: This review paper provides an insight into various methods employed using electroencephalogram (EEG), facial, and speech signals coupled with multi-modal emotion recognition techniques. In this work, we have reviewed most of the state-of-the-art papers published on this topic.

Method: This study was carried out by considering the various emotion recognition (ER) models proposed between 2016 and 2021. The papers were analysed based on methods employed, classifier used and performance obtained.

Results: There is a significant rise in the application of deep learning techniques for ER. They have been widely applied for EEG, speech, facial expression, and multimodal features to develop an accurate ER model.

Conclusion: Our study reveals that most of the proposed machine and deep learning-based systems have yielded good performances for automated ER in a controlled environment. However, there is a need to obtain high performance for ER even in an uncontrolled environment.
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http://dx.doi.org/10.1016/j.cmpb.2022.106646DOI Listing
March 2022

Automated identification of sleep disorders using wavelet-based features extracted from electrooculogram and electromyogram signals.

Comput Biol Med 2022 Jan 21;143:105224. Epub 2022 Jan 21.

School of Engineering, Ngee Ann Polytechnic, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan; School of Science and Technology, Singapore University of Social Sciences, Singapore. Electronic address:

Sleep is imperative for a healthy life as it rejuvenates memory, cognitive performance, cell repair and eliminates waste from the muscles. Sleep-related disorders such as insomnia, narcolepsy, sleep-disordered breathing (SDB), periodic leg movement (PLM), and bruxism lead to hormonal imbalance, slower reaction time, memory problems, depression, and headaches. This adversity of sleep disorder gained the attention of many sleep researchers. To examine the reasons for sleep disorders, it is imperative to monitor and analyze the sleep of the affected patients. The conventional method of monitoring sleep and identifying the sleep disorders using polysomnographic (PSG) recording is a complicated and cumbersome task in which multiple physiological signals with multiple modalities are recorded for a long (overnight) duration. The PSG recordings are carried out in sophisticated sleep laboratories and cannot be considered suitable for real-time sleep monitoring. Thus, a simple and patient-convenient system is highly desirable to monitor and analyze the quality of sleep. We proposed an automatic detection of sleep disorders using single modal electrooculogram (EOG) and electromyogram (EMG) signals. We have used a new maximally flat multiplier-less biorthogonal filter bank for obtaining discrete wavelet transform of the signals. We computed Hjorth parameters (HOP) such as activity, mobility, and complexity from the wavelet sub-bands. Highly discriminative HOP features are fed to different machine learning classifiers to develop the model. Our results show that the developed system can classify insomnia, narcolepsy, NFLE, PLM, and REM behaviour disorder (RBD) against normal healthy subjects with an accuracy of 99.7%, 97.6%, 97.5%, 97.5%, and 98.3%, respectively using combined features from EOG and EMG signal. The proposed model has yielded an accuracy of 94.3% in classifying six classes using an ensemble bagged trees classifier (EBTC) with a 10-fold cross-validation technique. Hence, EOG and EMG-based proposed methods can be deployed in a portable home-based environment to identify the type of sleep disorders automatically.
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http://dx.doi.org/10.1016/j.compbiomed.2022.105224DOI Listing
January 2022

High flow arteriovenous malformation with transverse-sigmoid sinus stenosis and congestive venopathy mimicking DAVF.

Br J Neurosurg 2022 Jan 7:1-4. Epub 2022 Jan 7.

Department of Neurosurgery, Manipal Hospitals, Bengaluru, India.

Background: Arteriovenous malformations commonly present with hemorrhage, seizures, headache and other symptomatology. However, AVMs presenting as venous hypertension, owing to downstream stenosis/occlusion of venous sinuses, are very rare. This presentation is much more common in patients with dural AVFs.

Case Description: We present a young lady with left frontal arteriovenous malformation with bilateral transverse-sigmoid sinus stenosis, presenting with features of venous hypertension, which was treated with surgical excision of AVM. This case demonstrates a rare example of AVM with co-existing venous sinus stenosis distal to the nidus.

Conclusions: High flow AVMs may co-exist with venous sinus stenosis or occlusion and lead to congestive venopathy. Treatment of AVM with surgical resection can be performed safely to relieve the hyper-dynamic venous congestion.
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http://dx.doi.org/10.1080/02688697.2021.2024500DOI Listing
January 2022

Interpretation of radiomics features-A pictorial review.

Comput Methods Programs Biomed 2022 Mar 27;215:106609. Epub 2021 Dec 27.

Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore; School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, 47500 Subang Jaya, Malaysia; Department of Biomedical Informatics and Medical Engineering, Asia University, Taiwan.

Radiomics is a newcomer field that has opened new windows for precision medicine. It is related to extraction of a large number of quantitative features from medical images, which may be difficult to detect visually. Underlying tumor biology can change physical properties of tissues, which affect patterns of image pixels and radiomics features. The main advantage of radiomics is that it can characterize the whole tumor non-invasively, even after a single sampling from an image. Therefore, it can be linked to a "digital biopsy". Physicians need to know about radiomics features to determine how their values correlate with the appearance of lesions and diseases. Indeed, physicians need practical references to conceive of basics and concepts of each radiomics feature without knowing their sophisticated mathematical formulas. In this review, commonly used radiomics features are illustrated with practical examples to help physicians in their routine diagnostic procedures.
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http://dx.doi.org/10.1016/j.cmpb.2021.106609DOI Listing
March 2022

A Review on Computer Aided Diagnosis of Acute Brain Stroke.

Sensors (Basel) 2021 Dec 20;21(24). Epub 2021 Dec 20.

Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia.

Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., 'ischemic penumbra') can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas.
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http://dx.doi.org/10.3390/s21248507DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8707263PMC
December 2021

Multilevel Deep Feature Generation Framework for Automated Detection of Retinal Abnormalities Using OCT Images.

Entropy (Basel) 2021 Dec 8;23(12). Epub 2021 Dec 8.

Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore.

Optical coherence tomography (OCT) images coupled with many learning techniques have been developed to diagnose retinal disorders. This work aims to develop a novel framework for extracting deep features from 18 pre-trained convolutional neural networks (CNN) and to attain high performance using OCT images. In this work, we have developed a new framework for automated detection of retinal disorders using transfer learning. This model consists of three phases: deep fused and multilevel feature extraction, using 18 pre-trained networks and tent maximal pooling, feature selection with ReliefF, and classification using the optimized classifier. The novelty of this proposed framework is the feature generation using widely used CNNs and to select the most suitable features for classification. The extracted features using our proposed intelligent feature extractor are fed to iterative ReliefF (IRF) to automatically select the best feature vector. The quadratic support vector machine (QSVM) is utilized as a classifier in this work. We have developed our model using two public OCT image datasets, and they are named database 1 (DB1) and database 2 (DB2). The proposed framework can attain 97.40% and 100% classification accuracies using the two OCT datasets, DB1 and DB2, respectively. These results illustrate the success of our model.
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http://dx.doi.org/10.3390/e23121651DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8700736PMC
December 2021

Automated classification of attention deficit hyperactivity disorder and conduct disorder using entropy features with ECG signals.

Comput Biol Med 2021 Dec 4;140:105120. Epub 2021 Dec 4.

Developmental Psychiatry, Institute of Mental Health, Singapore.

Background: The most prevalent neuropsychiatric disorder among children is attention deficit hyperactivity disorder (ADHD). ADHD presents with a high prevalence of comorbid disorders such as conduct disorder (CD). The lack of definitive confirmatory diagnostic tests for ADHD and CD make diagnosis challenging. The distinction between ADHD, ADHD + CD and CD is important as the course and treatment are different. Electrocardiography (ECG) signals may become altered in behavioral disorders due to brain-heart autonomic interactions. We have developed a software tool to categorize ADHD, ADHD + CD and CD automatically on ECG signals.

Method: ECG signals from participants were decomposed using empirical wavelet transform into various modes, from which entropy features were extracted. Robust ten-fold cross-validation with adaptive synthetic sampling (ADASYN) and z-score normalization were performed at each fold. Analysis of variance (ANOVA) technique was employed to determine the variability within the three classes, and obtained the most discriminatory features. Highly significant entropy features were then fed to classifiers.

Results: Our model yielded the best classification results with the bagged tree classifier: 87.19%, 87.71% and 86.29% for accuracy, sensitivity and specificity, respectively.

Conclusion: The proposed expert system can potentially assist mental health professionals in the stratification of the three classes, for appropriate intervention using accessible ECG signals.
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http://dx.doi.org/10.1016/j.compbiomed.2021.105120DOI Listing
December 2021

Role of Artificial Intelligence in COVID-19 Detection.

Sensors (Basel) 2021 Dec 1;21(23). Epub 2021 Dec 1.

School of Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore.

The global pandemic of coronavirus disease (COVID-19) has caused millions of deaths and affected the livelihood of many more people. Early and rapid detection of COVID-19 is a challenging task for the medical community, but it is also crucial in stopping the spread of the SARS-CoV-2 virus. Prior substantiation of artificial intelligence (AI) in various fields of science has encouraged researchers to further address this problem. Various medical imaging modalities including X-ray, computed tomography (CT) and ultrasound (US) using AI techniques have greatly helped to curb the COVID-19 outbreak by assisting with early diagnosis. We carried out a systematic review on state-of-the-art AI techniques applied with X-ray, CT, and US images to detect COVID-19. In this paper, we discuss approaches used by various authors and the significance of these research efforts, the potential challenges, and future trends related to the implementation of an AI system for disease detection during the COVID-19 pandemic.
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http://dx.doi.org/10.3390/s21238045DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8659534PMC
December 2021

Application of CycleGAN and transfer learning techniques for automated detection of COVID-19 using X-ray images.

Pattern Recognit Lett 2022 Jan 3;153:67-74. Epub 2021 Dec 3.

Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore.

Coronavirus (which is also known as COVID-19) is severely impacting the wellness and lives of many across the globe. There are several methods currently to detect and monitor the progress of the disease such as radiological image from patients' chests, measuring the symptoms and applying polymerase chain reaction (RT-PCR) test. X-ray imaging is one of the popular techniques used to visualise the impact of the virus on the lungs. Although manual detection of this disease using radiology images is more popular, it can be time-consuming, and is prone to human errors. Hence, automated detection of lung pathologies due to COVID-19 utilising deep learning (Bowles et al.) techniques can assist with yielding accurate results for huge databases. Large volumes of data are needed to achieve generalizable DL models; however, there are very few public databases available for detecting COVID-19 disease pathologies automatically. Standard data augmentation method can be used to enhance the models' generalizability. In this research, the Extensive COVID-19 X-ray and CT Chest Images Dataset has been used and generative adversarial network (GAN) coupled with trained, semi-supervised CycleGAN (SSA- CycleGAN) has been applied to augment the training dataset. Then a newly designed and finetuned Inception V3 transfer learning model has been developed to train the algorithm for detecting COVID-19 pandemic. The obtained results from the proposed Inception-CycleGAN model indicated Accuracy = 94.2%, Area under Curve = 92.2%, Mean Squared Error = 0.27, Mean Absolute Error = 0.16. The developed Inception-CycleGAN framework is ready to be tested with further COVID-19 X-Ray images of the chest.
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http://dx.doi.org/10.1016/j.patrec.2021.11.020DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8641403PMC
January 2022

Application of artificial intelligence in wearable devices: Opportunities and challenges.

Comput Methods Programs Biomed 2022 Jan 17;213:106541. Epub 2021 Nov 17.

Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan.

Background And Objectives: Wearable technologies have added completely new and fast emerging tools to the popular field of personal gadgets. Aside from being fashionable and equipped with advanced hardware technologies such as communication modules and networking, wearable devices have the potential to fuel artificial intelligence (AI) methods with a wide range of valuable data.

Methods: Various AI techniques such as supervised, unsupervised, semi-supervised and reinforcement learning (RL) have already been used to carry out various tasks. This paper reviews the recent applications of wearables that have leveraged AI to achieve their objectives.

Results: Particular example applications of supervised and unsupervised learning for medical diagnosis are reviewed. Moreover, examples combining the internet of things, wearables, and RL are reviewed. Application examples of wearables will be also presented for specific domains such as medical, industrial, and sport. Medical applications include fitness, movement disorder, mental health, etc. Industrial applications include employee performance improvement with the aid of wearables. Sport applications are all about providing better user experience during workout sessions or professional gameplays.

Conclusion: The most important challenges regarding design and development of wearable devices and the computation burden of using AI methods are presented. Finally, future challenges and opportunities for wearable devices are presented.
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http://dx.doi.org/10.1016/j.cmpb.2021.106541DOI Listing
January 2022

Automated COVID-19 and Heart Failure Detection Using DNA Pattern Technique with Cough Sounds.

Diagnostics (Basel) 2021 Oct 22;11(11). Epub 2021 Oct 22.

Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore.

COVID-19 and heart failure (HF) are common disorders and although they share some similar symptoms, they require different treatments. Accurate diagnosis of these disorders is crucial for disease management, including patient isolation to curb infection spread of COVID-19. In this work, we aim to develop a computer-aided diagnostic system that can accurately differentiate these three classes (normal, COVID-19 and HF) using cough sounds. A novel handcrafted model was used to classify COVID-19 vs. healthy (Case 1), HF vs. healthy (Case 2) and COVID-19 vs. HF vs. healthy (Case 3) automatically using deoxyribonucleic acid (DNA) patterns. The model was developed using the cough sounds collected from 241 COVID-19 patients, 244 HF patients, and 247 healthy subjects using a hand phone. To the best our knowledge, this is the first work to automatically classify healthy subjects, HF and COVID-19 patients using cough sounds signals. Our proposed model comprises a graph-based local feature generator (DNA pattern), an iterative maximum relevance minimum redundancy (ImRMR) iterative feature selector, with classification using the k-nearest neighbor classifier. Our proposed model attained an accuracy of 100.0%, 99.38%, and 99.49% for Case 1, Case 2, and Case 3, respectively. The developed system is completely automated and economical, and can be utilized to accurately detect COVID-19 versus HF using cough sounds.
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http://dx.doi.org/10.3390/diagnostics11111962DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8620352PMC
October 2021

Application of Deep Learning Models for Automated Identification of Parkinson's Disease: A Review (2011-2021).

Sensors (Basel) 2021 Oct 23;21(21). Epub 2021 Oct 23.

School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore.

Parkinson's disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic treatments that can increase the survivability of the disease, there are no curative treatments. The prevalence of PD and disability-adjusted life years continue to increase steadily, leading to a growing burden on patients, their families, society and the economy. Dopaminergic medications can significantly slow down the progression of PD when applied during the early stages. However, these treatments often become less effective with the disease progression. Early diagnosis of PD is crucial for immediate interventions so that the patients can remain self-sufficient for the longest period of time possible. Unfortunately, diagnoses are often late, due to factors such as a global shortage of neurologists skilled in early PD diagnosis. Computer-aided diagnostic (CAD) tools, based on artificial intelligence methods, that can perform automated diagnosis of PD, are gaining attention from healthcare services. In this review, we have identified 63 studies published between January 2011 and July 2021, that proposed deep learning models for an automated diagnosis of PD, using various types of modalities like brain analysis (SPECT, PET, MRI and EEG), and motion symptoms (gait, handwriting, speech and EMG). From these studies, we identify the best performing deep learning model reported for each modality and highlight the current limitations that are hindering the adoption of such CAD tools in healthcare. Finally, we propose new directions to further the studies on deep learning in the automated detection of PD, in the hopes of improving the utility, applicability and impact of such tools to improve early detection of PD globally.
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http://dx.doi.org/10.3390/s21217034DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8587636PMC
October 2021

Review of Deep Learning-Based Atrial Fibrillation Detection Studies.

Int J Environ Res Public Health 2021 10 28;18(21). Epub 2021 Oct 28.

Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 138607, Singapore.

Atrial fibrillation (AF) is a common arrhythmia that can lead to stroke, heart failure, and premature death. Manual screening of AF on electrocardiography (ECG) is time-consuming and prone to errors. To overcome these limitations, computer-aided diagnosis systems are developed using artificial intelligence techniques for automated detection of AF. Various machine learning and deep learning (DL) techniques have been developed for the automated detection of AF. In this review, we focused on the automated AF detection models developed using DL techniques. Twenty-four relevant articles published in international journals were reviewed. DL models based on deep neural network, convolutional neural network (CNN), recurrent neural network, long short-term memory, and hybrid structures were discussed. Our analysis showed that the majority of the studies used CNN models, which yielded the highest detection performance using ECG and heart rate variability signals. Details of the ECG databases used in the studies, performance metrics of the various models deployed, associated advantages and limitations, as well as proposed future work were summarized and discussed. This review paper serves as a useful resource for the researchers interested in developing innovative computer-assisted ECG-based DL approaches for AF detection.
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http://dx.doi.org/10.3390/ijerph182111302DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8583162PMC
October 2021

Deep learning for neuroimaging-based diagnosis and rehabilitation of Autism Spectrum Disorder: A review.

Comput Biol Med 2021 12 29;139:104949. Epub 2021 Oct 29.

Deakin University, IMPACT - the Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, Geelong, Australia; Orygen, The National Centre of Excellence in Youth Mental Health, Centre for Youth Mental Health, Florey Institute for Neuroscience and Mental Health and the Department of Psychiatry, The University of Melbourne, Melbourne, Australia.

Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) techniques can aid physicians to apply automatic diagnosis and rehabilitation procedures. AI techniques comprise traditional machine learning (ML) approaches and deep learning (DL) techniques. Conventional ML methods employ various feature extraction and classification techniques, but in DL, the process of feature extraction and classification is accomplished intelligently and integrally. DL methods for diagnosis of ASD have been focused on neuroimaging-based approaches. Neuroimaging techniques are non-invasive disease markers potentially useful for ASD diagnosis. Structural and functional neuroimaging techniques provide physicians substantial information about the structure (anatomy and structural connectivity) and function (activity and functional connectivity) of the brain. Due to the intricate structure and function of the brain, proposing optimum procedures for ASD diagnosis with neuroimaging data without exploiting powerful AI techniques like DL may be challenging. In this paper, studies conducted with the aid of DL networks to distinguish ASD are investigated. Rehabilitation tools provided for supporting ASD patients utilizing DL networks are also assessed. Finally, we will present important challenges in the automated detection and rehabilitation of ASD and propose some future works.
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http://dx.doi.org/10.1016/j.compbiomed.2021.104949DOI Listing
December 2021

Probing Gluon Spin-Momentum Correlations in Transversely Polarized Protons through Midrapidity Isolated Direct Photons in p^{↑}+p Collisions at sqrt[s]=200  GeV.

Phys Rev Lett 2021 Oct;127(16):162001

University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, USA.

Studying spin-momentum correlations in hadronic collisions offers a glimpse into a three-dimensional picture of proton structure. The transverse single-spin asymmetry for midrapidity isolated direct photons in p^{↑}+p collisions at sqrt[s]=200  GeV is measured with the PHENIX detector at the Relativistic Heavy Ion Collider (RHIC). Because direct photons in particular are produced from the hard scattering and do not interact via the strong force, this measurement is a clean probe of initial-state spin-momentum correlations inside the proton and is in particular sensitive to gluon interference effects within the proton. This is the first time direct photons have been used as a probe of spin-momentum correlations at RHIC. The uncertainties on the results are a 50-fold improvement with respect to those of the one prior measurement for the same observable, from the Fermilab E704 experiment. These results constrain gluon spin-momentum correlations in transversely polarized protons.
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http://dx.doi.org/10.1103/PhysRevLett.127.162001DOI Listing
October 2021

Multi-Scale Convolutional Neural Network for Accurate Corneal Segmentation in Early Detection of Fungal Keratitis.

J Fungi (Basel) 2021 Oct 11;7(10). Epub 2021 Oct 11.

School of Engineering, Ngee Ann Polytechnic, Clementi 599489, Singapore.

Microbial keratitis is an infection of the cornea of the eye that is commonly caused by prolonged contact lens wear, corneal trauma, pre-existing systemic disorders and other ocular surface disorders. It can result in severe visual impairment if improperly managed. According to the latest World Vision Report, at least 4.2 million people worldwide suffer from corneal opacities caused by infectious agents such as fungi, bacteria, protozoa and viruses. In patients with fungal keratitis (FK), often overt symptoms are not evident, until an advanced stage. Furthermore, it has been reported that clear discrimination between bacterial keratitis and FK is a challenging process even for trained corneal experts and is often misdiagnosed in more than 30% of the cases. However, if diagnosed early, vision impairment can be prevented through early cost-effective interventions. In this work, we propose a multi-scale convolutional neural network (MS-CNN) for accurate segmentation of the corneal region to enable early FK diagnosis. The proposed approach consists of a deep neural pipeline for corneal region segmentation followed by a ResNeXt model to differentiate between FK and non-FK classes. The model trained on the segmented images in the region of interest, achieved a diagnostic accuracy of 88.96%. The features learnt by the model emphasize that it can correctly identify dominant corneal lesions for detecting FK.
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http://dx.doi.org/10.3390/jof7100850DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8540278PMC
October 2021

Spectrum of intracranial complications of rhino-orbito-cerebral mucormycosis - resurgence in the era of COVID-19 pandemic: a pictorial essay.

Emerg Radiol 2021 Dec 4;28(6):1097-1106. Epub 2021 Oct 4.

Department of Radiology, Manipal Hospitals, 98, HAL Old Airport Road, Kodihalli, Bengaluru, 560017, India.

Rhino-orbito-cerebral mucormycosis (ROCM) has regained significance following its resurgence in the second wave of the COVID-19 pandemic in India. Rapid and progressive intracranial spread occurs either by direct extension across the neural foraminae, cribriform plate/ethmoid, walls of sinuses, or angioinvasion. Having known to have a high mortality rate, especially with intracranial extension of disease, it becomes imperative to familiarise oneself with its imaging features. MRI is the imaging modality of choice. This pictorial essay aims to depict and detail the various intracranial complications of mucormycosis and to serve as a broad checklist of structures and pathologies that must be looked for in a known or suspected case of ROCM.
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http://dx.doi.org/10.1007/s10140-021-01987-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8488318PMC
December 2021

A practical artificial intelligence system to diagnose COVID-19 using computed tomography: A multinational external validation study.

Pattern Recognit Lett 2021 Dec 23;152:42-49. Epub 2021 Sep 23.

Department of Radiology, Faculty of Medicine, Urmia University of Medical Science, Urmia, Iran.

Computed tomography has gained an important role in the early diagnosis of COVID-19 pneumonia. However, the ever-increasing number of patients has overwhelmed radiology departments and has caused a reduction in quality of services. Artificial intelligence (AI) systems are the remedy to the current situation. However, the lack of application in real-world conditions has limited their consideration in clinical settings. This study validated a clinical AI system, COVIDiag, to aid radiologists in accurate and rapid evaluation of COVID-19 cases. 50 COVID-19 and 50 non-COVID-19 pneumonia cases were included from each of five centers: Argentina, Turkey, Iran, Netherlands, and Italy. The Dutch database included only 50 COVID-19 cases. The performance parameters namely sensitivity, specificity, accuracy, and area under the ROC curve (AUC) were computed for each database using COVIDiag model. The most common pattern of involvement among COVID-19 cases in all databases were bilateral involvement of upper and lower lobes with ground-glass opacities. The best sensitivity of 92.0% was recorded for the Italian database. The system achieved an AUC of 0.983, 0.914, 0.910, and 0.882 for Argentina, Turkey, Iran, and Italy, respectively. The model obtained a sensitivity of 86.0% for the Dutch database. COVIDiag model could diagnose COVID-19 pneumonia in all of cohorts with AUC of 0.921 (sensitivity, specificity, and accuracy of 88.8%, 87.0%, and 88.0%, respectively). Our study confirmed the accuracy of our proposed AI model (COVIDiag) in the diagnosis of COVID-19 cases. Furthermore, the system demonstrated consistent optimal diagnostic performance on multinational databases, which is critical to determine the generalizability and objectivity of the proposed COVIDiag model. Our results are significant as they provide real-world evidence regarding the applicability of AI systems in clinical medicine.
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http://dx.doi.org/10.1016/j.patrec.2021.09.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8457921PMC
December 2021

Synthesis and Impedance Spectroscopy of Poly(-phenylenediamine)/Montmorillonite Composites.

Polymers (Basel) 2021 Sep 16;13(18). Epub 2021 Sep 16.

Institute of Macromolecular Chemistry, Czech Academy of Sciences, 162 06 Prague, Czech Republic.

Poly(-phenylenediamine)/montmorillonite (PPDA/MMT) composites were prepared by the oxidative polymerization of monomers intercalated within the MMT gallery, using ammonium peroxydisulfate as an oxidant. The intercalation process was evidenced by X-ray powder diffraction. The FT-IR and Raman spectroscopies revealed that, depending on the initial ratio between monomers and MMT in the polymerization mixture, the polymer or mainly oligomers are created during polymerization. The DC conductivity of composites was found to be higher than the conductivity of pristine polymer, reaching the highest value of 10 S cm for the optimal MMT amount used during polymerization. Impedance spectroscopy was performed over wide frequency and temperature ranges to study the charge transport mechanism. The data analyzed in the framework of conductivity formalism suggest different conduction mechanisms for high and low temperature regions.
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http://dx.doi.org/10.3390/polym13183132DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8469202PMC
September 2021

PrimePatNet87: Prime pattern and tunable q-factor wavelet transform techniques for automated accurate EEG emotion recognition.

Comput Biol Med 2021 11 16;138:104867. Epub 2021 Sep 16.

Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore; Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan. Electronic address:

Nowadays, many deep models have been presented to recognize emotions using electroencephalogram (EEG) signals. These deep models are computationally intensive, it takes a longer time to train the model. Also, it is difficult to achieve high classification performance using for emotion classification using machine learning techniques. To overcome these limitations, we present a hand-crafted conventional EEG emotion classification network. In this work, we have used novel prime pattern and tunable q-factor wavelet transform (TQWT) techniques to develop an automated model to classify human emotions. Our proposed cognitive model comprises feature extraction, feature selection, and classification steps. We have used TQWT on the EEG signals to obtain the sub-bands. The prime pattern and statistical feature generator are employed on the generated sub-bands and original signal to generate 798 features. 399 (half of them) out of 798 features are selected using minimum redundancy maximum relevance (mRMR) selector, and misclassification rates of each signal are evaluated using support vector machine (SVM) classifier. The proposed network generated 87 feature vectors hence, this model is named PrimePatNet87. In the last step of the feature generation, the best 20 feature vectors which are selected based on the calculated misclassification rates, are concatenated. The generated feature vector is subjected to the feature selection and the most significant 1000 features are selected using the mRMR selector. These selected features are then classified using an SVM classifier. In the last phase, iterative majority voting has been used to generate a general result. We have used three publicly available datasets, namely DEAP, DREAMER, and GAMEEMO, to develop our proposed model. Our presented PrimePatNet87 model reached over 99% classification accuracy on whole datasets with leave one subject out (LOSO) validation. Our results demonstrate that the developed prime pattern network is accurate and ready for real-world applications.
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http://dx.doi.org/10.1016/j.compbiomed.2021.104867DOI Listing
November 2021

Novel automated PD detection system using aspirin pattern with EEG signals.

Comput Biol Med 2021 10 6;137:104841. Epub 2021 Sep 6.

Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore; Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan. Electronic address:

Background And Objective: Parkinson's disease (PD) is one of the most common diseases worldwide which reduces quality of life of patients and their family members. The electroencephalogram (EEG) signals coupled with various advanced machine-learning algorithms have been widely used to detect PD automatically. In this paper, we propose a novel aspirin pattern to detect PD accurately using EEG signals.

Method: In this research, the feature generation ability of a chemical graph is investigated. Therefore, this work presents a new graph-based aspirin model for automated PD detection using EEG signals. The proposed method consists of (i) multilevel feature generation phase involving new aspirin pattern, statistical moments, and maximum absolute pooling (MAP), (ii) selection of most discriminative features using neighborhood component analysis (NCA), and (iii) classification using k nearest neighbor (kNN) for automated detection of PD and (iv) iterative majority voting.

Results: A public dataset has been used to develop the proposed model. Two cases are created, and these cases consisted of two classes. Leave one subject out (LOSO) validation have been used to calculate robust results. Our proposal achieved 93.57% and 95.48% classification accuracies for Case 1 and Case 2 respectively.

Conclusion: Our developed automated PD model is accurate and equipped to be tested with more diverse EEG datasets.
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http://dx.doi.org/10.1016/j.compbiomed.2021.104841DOI Listing
October 2021

Novel ensemble of optimized CNN and dynamic selection techniques for accurate Covid-19 screening using chest CT images.

Comput Biol Med 2021 10 6;137:104835. Epub 2021 Sep 6.

Dept. of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore; Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore; Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan. Electronic address:

The world is significantly affected by infectious coronavirus disease (covid-19). Timely prognosis and treatment are important to control the spread of this infection. Unreliable screening systems and limited number of clinical facilities are the major hurdles in controlling the spread of covid-19. Nowadays, many automated detection systems based on deep learning techniques using computed tomography (CT) images have been proposed to detect covid-19. However, these systems have the following drawbacks: (i) limited data problem poses a major hindrance to train the deep neural network model to provide accurate diagnosis, (ii) random choice of hyperparameters of Convolutional Neural Network (CNN) significantly affects the classification performance, since the hyperparameters have to be application dependent and, (iii) the generalization ability using CNN classification is usually not validated. To address the aforementioned issues, we propose two models: (i) based on a transfer learning approach, and (ii) using novel strategy to optimize the CNN hyperparameters using Whale optimization-based BAT algorithm + AdaBoost classifier built using dynamic ensemble selection techniques. According to our second method depending on the characteristics of test sample, the classifier is chosen, thereby reducing the risk of overfitting and simultaneously produced promising results. Our proposed methodologies are developed using 746 CT images. Our method obtained a sensitivity, specificity, accuracy, F-1 score, and precision of 0.98, 0.97, 0.98, 0.98, and 0.98, respectively with five-fold cross-validation strategy. Our developed prototype is ready to be tested with huge chest CT images database before its real-world application.
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http://dx.doi.org/10.1016/j.compbiomed.2021.104835DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8418990PMC
October 2021
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