10,799 results match your criteria feature extraction


Evolution of Deep Learning Algorithms for MRI-Based Brain Tumor Image Segmentation.

Crit Rev Biomed Eng 2021 ;49(1):77-94

Department of Electronics and Communication Engineering, Delhi Technological University.

Brain tumor textures are among the most challenging features for neuroradiologists to extract from magnetic resonance images (MRIs). Exceptionally high-grade tumors such as gliomas require quick and precise diagnosis and medical intervention due to their infiltrative and fast-spreading nature. Therefore, they require computer assistance instead of manual methods. Read More

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January 2021

Feature extraction and machine learning techniques for identifying historic urban environmental hazards: New methods to locate lost fossil fuel infrastructure in US cities.

PLoS One 2021 4;16(8):e0255507. Epub 2021 Aug 4.

Center for Computation and Visualization, Brown University, Providence, Rhode Island, United States of America.

U.S. cities contain unknown numbers of undocumented "manufactured gas" sites, legacies of an industry that dominated energy production during the late-19th and early-20th centuries. Read More

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Deep Hybrid 2-D-3-D CNN Based on Dual Second-Order Attention With Camera Spectral Sensitivity Prior for Spectral Super-Resolution.

IEEE Trans Neural Netw Learn Syst 2021 Aug 4;PP. Epub 2021 Aug 4.

A largely ignored fact in spectral super-resolution (SSR) is that the subsistent mapping methods neglect the auxiliary prior of camera spectral sensitivity (CSS) and only pay attention to wider or deeper network framework design while ignoring to excavate the spatial and spectral dependencies among intermediate layers, hence constraining representational capability of convolutional neural networks (CNNs). To conquer these drawbacks, we propose a novel deep hybrid 2-D-3-D CNN based on dual second-order attention with CSS prior (HSACS), which can excavate sufficient spatial-spectral context information. Specifically, dual second-order attention embedded in the residual block for more powerful spatial-spectral feature representation and relation learning is composed of a brand new trainable 2-D second-order channel attention (SCA) or 3-D second-order band attention (SBA) and a structure tensor attention (STA). Read More

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Automatic segmentation of lung tumors on CT images based on a 2D & 3D hybrid convolutional neural network.

Br J Radiol 2021 Aug 4:20210038. Epub 2021 Aug 4.

Department of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.

Objective: A stable and accurate automatic tumor delineation method has been developed to facilitate the intelligent design of lung cancer radiotherapy process. The purpose of this paper is to introduce an automatic tumor segmentation network for lung cancer on CT images based on deep learning.

Methods: In this paper, a hybrid convolution neural network (CNN) combining 2D CNN and 3D CNN was implemented for the automatic lung tumor delineation using CT images. Read More

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Association predictions of genomics, proteinomics, transcriptomics, microbiome, metabolomics, pathomics, radiomics, drug, symptoms, environment factor, and disease networks: A comprehensive approach.

Med Res Rev 2021 Aug 4. Epub 2021 Aug 4.

School of Computer Science, Shaanxi Normal University, Xi'an, China.

Currently, the research of multi-omics, such as genomics, proteinomics, transcriptomics, microbiome, metabolomics, pathomics, and radiomics, are hot spots. The relationship between multi-omics data, drugs, and diseases has received extensive attention from researchers. At the same time, multi-omics can effectively predict the diagnosis, prognosis, and treatment of diseases. Read More

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Elucidating the chemical and structural composition of breast cancer using Raman micro-spectroscopy.

EXCLI J 2021 2;20:1118-1132. Epub 2021 Jul 2.

Engineering Department, Faculty of Science and Technology, Lancaster University, Lancaster LA1 4YW, U.K.

The current gold standard for breast cancer (BC) diagnosis is the histopathological assessment of biopsy samples. However, this approach limits the understanding of the disease in terms of biochemical changes. Raman spectroscopy has demonstrated its potential to provide diagnostic information and facilitate the prediction of the biochemical progression for different diseases in a rapid non-destructive manner. Read More

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Bioacoustic classification of avian calls from raw sound waveforms with an open-source deep learning architecture.

Sci Rep 2021 Aug 3;11(1):15733. Epub 2021 Aug 3.

School of Engineering and Technology, Central Queensland University, North Rockhampton, QLD, Australia.

The use of autonomous recordings of animal sounds to detect species is a popular conservation tool, constantly improving in fidelity as audio hardware and software evolves. Current classification algorithms utilise sound features extracted from the recording rather than the sound itself, with varying degrees of success. Neural networks that learn directly from the raw sound waveforms have been implemented in human speech recognition but the requirements of detailed labelled data have limited their use in bioacoustics. Read More

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Sch-net: a deep learning architecture for automatic detection of schizophrenia.

Biomed Eng Online 2021 Aug 3;20(1):75. Epub 2021 Aug 3.

School of Cybersecurity, Chengdu University of Information Technology, Chengdu, China.

Background: Schizophrenia is a chronic and severe mental disease, which largely influences the daily life and work of patients. Clinically, schizophrenia with negative symptoms is usually misdiagnosed. The diagnosis is also dependent on the experience of clinicians. Read More

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Artificial intelligence-driven assessment of radiological images for COVID-19.

Comput Biol Med 2021 Jul 21;136:104665. Epub 2021 Jul 21.

Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada; Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada.

Artificial Intelligence (AI) methods have significant potential for diagnosis and prognosis of COVID-19 infections. Rapid identification of COVID-19 and its severity in individual patients is expected to enable better control of the disease individually and at-large. There has been remarkable interest by the scientific community in using imaging biomarkers to improve detection and management of COVID-19. Read More

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A CNN model embedded with local feature knowledge and its application to time-varying signal classification.

Neural Netw 2021 Jul 22;142:564-572. Epub 2021 Jul 22.

College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China.

A novel convolutional neural network is proposed for local prior feature embedding and imbalanced dataset modeling for multi-channel time-varying signal classification. This model consists of a single-channel signal feature parallel extraction unit, a multi-channel signal feature integration unit, a local feature embedding and feature similarity measurement unit, a full connection layer, and a Softmax classifier. An algorithm combining dynamic clustering and sliding window was used to select segments signals with typical local features in each pattern class, forming a typical local feature set. Read More

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Identifying COVID-19 by using spectral analysis of cough recordings: a distinctive classification study.

Cogn Neurodyn 2021 Jul 29:1-15. Epub 2021 Jul 29.

Department of Electrical and Electronics Engineering, Faculty of Engineering, Avrasya University, 61080 Trabzon, Turkey.

Sound signals from the respiratory system are largely taken as tokens of human health. Early diagnosis of respiratory tract diseases is of great importance because, if delayed, it exerts irreversible effects on human health. The Coronavirus pandemic, which is deeply shaking the world, has revealed the importance of this diagnosis even more. Read More

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Bone morphological feature extraction for customized bone plate design.

Sci Rep 2021 Aug 2;11(1):15617. Epub 2021 Aug 2.

School of Medical Information and Engineering, Xuzhou Medical University, Xuzhou, 221004, People's Republic of China.

Fractures are difficult to treat because of individual differences in bone morphology and fracture types. Compared to serialized bone plates, the use of customized plates significantly improves the fracture healing process. However, designing custom plates often requires the extraction of skeletal morphology, which is a complex and time-consuming procedure. Read More

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Evaluation of aspiration problems in L2 English pronunciation employing machine learning.

J Acoust Soc Am 2021 07;150(1):120

Audio Acoustics Laboratory, Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, Gdańsk, Poland.

The approach proposed in this study includes methods specifically dedicated to the detection of allophonic variation in English. This study aims to find an efficient method for automatic evaluation of aspiration in the case of Polish second-language (L2) English speakers' pronunciation when whole words are analyzed instead of particular allophones extracted from words. Sample words including aspirated and unaspirated allophones were prepared by experts in English phonetics and phonology. Read More

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Prediction of diabetic protein markers based on an ensemble method.

Front Biosci (Landmark Ed) 2021 Jul;26(7):207-221

School of Opto-electronic and Communication Engineering, Xiamen University of Technology, 361024 Xiamen, Fujian, China.

: A diabetic protein marker is a type of protein that is closely related to diabetes. This kind of protein plays an important role in the prevention and diagnosis of diabetes. Therefore, it is necessary to identify an effective method for predicting diabetic protein markers. Read More

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Joint fully convolutional and graph convolutional networks for weakly-supervised segmentation of pathology images.

Med Image Anal 2021 Jul 24;73:102183. Epub 2021 Jul 24.

Tencent AI Lab, Shenzhen, Guangdong 518057, China. Electronic address:

Tissue/region segmentation of pathology images is essential for quantitative analysis in digital pathology. Previous studies usually require full supervision (e.g. Read More

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Joint Denoising and Demosaicking with Green Channel Prior for Real-world Burst Images.

IEEE Trans Image Process 2021 Aug 2;PP. Epub 2021 Aug 2.

Denoising and demosaicking are essential yet correlated steps to reconstruct a full color image from the raw color filter array (CFA) data. By learning a deep convolutional neural network (CNN), significant progress has been achieved to perform denoising and demosaicking jointly. However, most existing CNN-based joint denoising and demosaicking (JDD) methods work on a single image while assuming additive white Gaussian noise, which limits their performance on real-world applications. Read More

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A Hybrid Method of Covid-19 Patient Detection from Modified CT-Scan/Chest-X-Ray Images Combining Deep Convolutional Neural Network And Two- Dimensional Empirical Mode Decomposition.

Authors:
Nahian Ibn Hasan

Comput Methods Programs Biomed Update 2021 23;1:100022. Epub 2021 Jul 23.

Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.

The outbreak of the SARS-CoV-2/Covid-19 virus in 2019-2020 has made the world look for fast and accurate detection methods of the disease. The most commonly used tools for detecting Covid patients are Chest-X-ray or Chest-CT-scans of the patient. However, sometimes it's hard for the physicians to diagnose the SARS-CoV-2 infection from the raw image. Read More

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Blockchain technologies to mitigate COVID-19 challenges: A scoping review.

Comput Methods Programs Biomed Update 2021 14;1:100001. Epub 2020 Dec 14.

Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar.

As public health strategists and policymakers explore different approaches to lessen the devastating effects of novel coronavirus disease (COVID-19), blockchain technology has emerged as a resource that can be utilized in numerous ways. Many blockchain technologies have been proposed or implemented during the COVID-19 pandemic; however, to the best of our knowledge, no comprehensive reviews have been conducted to uncover and summarise the main feature of these technologies. This study aims to explore proposed or implemented blockchain technologies used to mitigate the COVID-19 challenges as reported in the literature. Read More

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December 2020

Breast Cancer Segmentation Methods: Current Status and Future Potentials.

Biomed Res Int 2021 20;2021:9962109. Epub 2021 Jul 20.

Radiology Department, Affiliated Hospital of Guizhou, Medical Hospital, China.

Early breast cancer detection is one of the most important issues that need to be addressed worldwide as it can help increase the survival rate of patients. Mammograms have been used to detect breast cancer in the early stages; if detected in the early stages, it can drastically reduce treatment costs. The detection of tumours in the breast depends on segmentation techniques. Read More

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Study Progress of Noninvasive Imaging and Radiomics for Decoding the Phenotypes and Recurrence Risk of Bladder Cancer.

Front Oncol 2021 15;11:704039. Epub 2021 Jul 15.

School of Biomedical Engineering, Air Force Medical University, Xi'an, China.

Urinary bladder cancer (BCa) is a highly prevalent disease among aged males. Precise diagnosis of tumor phenotypes and recurrence risk is of vital importance in the clinical management of BCa. Although imaging modalities such as CT and multiparametric MRI have played an essential role in the noninvasive diagnosis and prognosis of BCa, radiomics has also shown great potential in the precise diagnosis of BCa and preoperative prediction of the recurrence risk. Read More

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Intelligent predictions of Covid disease based on lung CT images using machine learning strategy.

Mater Today Proc 2021 Jul 27. Epub 2021 Jul 27.

Manipur International University, Manipur, India.

Covid or Corona Virus, a term ruling the world from past two years and causes a huge destruction in all countries. One of the most important Covid disease identification method is Lung based Computed Tomography (CT) image scanning, in which it provides an effective disease identification means in clear manner. However, this Lung CT image based disease detection principles are complex to health care representatives and doctors to predict the Covid disease accurately. Read More

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Comparison of Geographical Traceability of Wild and Cultivated with Different Data Fusion Approaches.

J Anal Methods Chem 2021 21;2021:5818999. Epub 2021 Jul 21.

Quality Standards and Testing Technology Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650205, China.

Poria originated from the dried sclerotium of is an edible traditional Chinese medicine with high economic value. Due to the significant difference in quality between wild and cultivated , this study aimed to trace the origin of the fungus from the perspectives of wild and cultivation. In addition, there were quite limited studies about data fusion, a potential strategy, employed and discussed in the geographical traceability of . Read More

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Thyroid Nodule Classification in Ultrasound Images by Fusion of Conventional Features and Res-GAN Deep Features.

Authors:
Yuan Hang

J Healthc Eng 2021 22;2021:9917538. Epub 2021 Jul 22.

School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.

In spite of the gargantuan number of patients affected by the thyroid nodule, the detection at an early stage is still a challenging task. Thyroid ultrasonography (US) is a noninvasive, inexpensive procedure widely used to detect and evaluate the thyroid nodules. The ultrasonography method for image classification is a computer-aided diagnostic technology based on image features. Read More

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An Intelligent Heartbeat Classification System Based on Attributable Features with AdaBoost+Random Forest Algorithm.

J Healthc Eng 2021 9;2021:9913127. Epub 2021 Jul 9.

Cooperative Innovation Center for Internet Healthcare, Zhengzhou University, Zhengzhou 450000, China.

Arrhythmia is a common cardiovascular disease that can threaten human life. In order to assist doctors in accurately diagnosing arrhythmia, an intelligent heartbeat classification system based on the selected optimal feature sets and AdaBoost + Random Forest model is developed. This system can acquire ECG signals through the Holter and transmit them to the cloud platform for preprocessing and feature extraction, and the features are input into AdaBoost + Random Forest for heartbeat classification. Read More

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Dual-Threshold-Based Microstate Analysis on Characterizing Temporal Dynamics of Affective Process and Emotion Recognition From EEG Signals.

Front Neurosci 2021 14;15:689791. Epub 2021 Jul 14.

Heilongjiang Provincial Hospital, Harbin, China.

Recently, emotion classification from electroencephalogram (EEG) data has attracted much attention. As EEG is an unsteady and rapidly changing voltage signal, the features extracted from EEG usually change dramatically, whereas emotion states change gradually. Most existing feature extraction approaches do not consider these differences between EEG and emotion. Read More

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Investigation on enhanced mathematical morphological operators for bearing fault feature extraction.

ISA Trans 2021 Jul 19. Epub 2021 Jul 19.

State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, China. Electronic address:

Morphological filtering has been extensively applied to rotating machinery diagnostics, whereas traditional morphological operators cannot effectively extract fault-triggered transient impulse components from noisy mechanical vibration signal. In this paper, a framework of generalized compound morphological operator (GCMO) is presented to enhance the extraction ability of impulsive fault features. Further, several new compound morphological operators are developed for transient impulse extraction by introducing the product, convolution, and cross-correlation operations into the GCMO framework. Read More

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A particle swarm optimization improved BP neural network intelligent model for electrocardiogram classification.

BMC Med Inform Decis Mak 2021 Jul 30;21(Suppl 2):99. Epub 2021 Jul 30.

Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou, 510006, China.

Background: As proven to reflect the work state of heart and physiological situation objectively, electrocardiogram (ECG) is widely used in the assessment of human health, especially the diagnosis of heart disease. The accuracy and reliability of abnormal ECG (AECG) decision depend to a large extent on the feature extraction. However, it is often uneasy or even impossible to obtain accurate features, as the detection process of ECG is easily disturbed by the external environment. Read More

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Learning rich features with hybrid loss for brain tumor segmentation.

BMC Med Inform Decis Mak 2021 Jul 30;21(Suppl 2):63. Epub 2021 Jul 30.

School of Information Science and Technology, and Centers for Biomedical Engineering, University of Science and Technology of China, Hefei, 230027, China.

Background: Accurately segment the tumor region of MRI images is important for brain tumor diagnosis and radiotherapy planning. At present, manual segmentation is wildly adopted in clinical and there is a strong need for an automatic and objective system to alleviate the workload of radiologists.

Methods: We propose a parallel multi-scale feature fusing architecture to generate rich feature representation for accurate brain tumor segmentation. Read More

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Investigating the impact of the CT Hounsfield unit range on radiomic feature stability using dual energy CT data.

Phys Med 2021 Jul 26;88:272-277. Epub 2021 Jul 26.

McGill University, Medical Physics Unit, Montreal, Canada.

Purpose: Radiomic texture calculation requires discretizing image intensities within the region-of-interest. FBN (fixed-bin-number), FBS (fixed-bin-size) and FBN and FBS with intensity equalization (FBNequal, FBSequal) are four discretization approaches. A crucial choice is the voxel intensity (Hounsfield units, or HU) binning range. Read More

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Use of wearable sensors to identify biomechanical alterations in runners with Exercise-Related lower leg pain.

J Biomech 2021 Jul 23;126:110646. Epub 2021 Jul 23.

University of Virginia School of Education Department of Kinesiology, Exercise and Sport Injury Lab, 210 Emmet Street South, Charlottesville, VA 22904, USA.

Exercise-related lower leg pain (ERLLP) is one of the most prevalent running-related injuries, however little is known about injured runners' mechanics during outdoor running. Establishing biomechanical alterations among ERLLP runners would help guide clinical interventions. Therefore, we sought to a) identify defining biomechanical features among ERLLP runners compared to healthy runners during outdoor running, and b) identify biomechanical thresholds to generate objective gait-training recommendations. Read More

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