Publications by authors named "Mudassar Raza"

7 Publications

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From ECG signals to images: a transformation based approach for deep learning.

PeerJ Comput Sci 2021 10;7:e386. Epub 2021 Feb 10.

Department of Applied Informatics, Vytautas Magnus University, Kaunas, Lithuania.

Provocative heart disease is related to ventricular arrhythmias (VA). Ventricular tachyarrhythmia is an irregular and fast heart rhythm that emerges from inappropriate electrical impulses in the ventricles of the heart. Different types of arrhythmias are associated with different patterns, which can be identified. An electrocardiogram (ECG) is the major analytical tool used to interpret and record ECG signals. ECG signals are nonlinear and difficult to interpret and analyze. We propose a new deep learning approach for the detection of VA. Initially, the ECG signals are transformed into images that have not been done before. Later, these images are normalized and utilized to train the AlexNet, VGG-16 and Inception-v3 deep learning models. Transfer learning is performed to train a model and extract the deep features from different output layers. After that, the features are fused by a concatenation approach, and the best features are selected using a heuristic entropy calculation approach. Finally, supervised learning classifiers are utilized for final feature classification. The results are evaluated on the MIT-BIH dataset and achieved an accuracy of 97.6% (using Cubic Support Vector Machine as a final stage classifier).
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http://dx.doi.org/10.7717/peerj-cs.386DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7959637PMC
February 2021

Detection and Classification of Gastrointestinal Diseases using Machine Learning.

Curr Med Imaging 2021 ;17(4):479-490

Department of Computer Science, Hitec University Taxila, Taxila, Pakistan.

Background: Traditional endoscopy is an invasive and painful method of examining the gastrointestinal tract (GIT) not supported by physicians and patients. To handle this issue, video endoscopy (VE) or wireless capsule endoscopy (WCE) is recommended and utilized for GIT examination. Furthermore, manual assessment of captured images is not possible for an expert physician because it's a time taking task to analyze thousands of images thoroughly. Hence, there comes the need for a Computer-Aided-Diagnosis (CAD) method to help doctors analyze images. Many researchers have proposed techniques for automated recognition and classification of abnormality in captured images.

Methods: In this article, existing methods for automated classification, segmentation and detection of several GI diseases are discussed. Paper gives a comprehensive detail about these state-of-theart methods. Furthermore, literature is divided into several subsections based on preprocessing techniques, segmentation techniques, handcrafted features based techniques and deep learning based techniques. Finally, issues, challenges and limitations are also undertaken.

Results: A comparative analysis of different approaches for the detection and classification of GI infections.

Conclusion: This comprehensive review article combines information related to a number of GI diseases diagnosis methods at one place. This article will facilitate the researchers to develop new algorithms and approaches for early detection of GI diseases detection with more promising results as compared to the existing ones of literature.
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http://dx.doi.org/10.2174/1573405616666200928144626DOI Listing
January 2021

Brain Tumor Detection by Using Stacked Autoencoders in Deep Learning.

J Med Syst 2019 Dec 17;44(2):32. Epub 2019 Dec 17.

Division of Computer Science, Mathematics and Science, Collins College of Professional Studies, St. John's University, New York, USA.

Brain tumor detection depicts a tough job because of its shape, size and appearance variations. In this manuscript, a deep learning model is deployed to predict input slices as a tumor (unhealthy)/non-tumor (healthy). This manuscript employs a high pass filter image to prominent the inhomogeneities field effect of the MR slices and fused with the input slices. Moreover, the median filter is applied to the fused slices. The resultant slices quality is improved with smoothen and highlighted edges of the input slices. After that, based on these slices' intensity, a 4-connected seed growing algorithm is applied, where optimal threshold clusters the similar pixels from the input slices. The segmented slices are then supplied to the fine-tuned two layers proposed stacked sparse autoencoder (SSAE) model. The hyperparameters of the model are selected after extensive experiments. At the first layer, 200 hidden units and at the second layer 400 hidden units are utilized. The testing is performed on the softmax layer for the prediction of the images having tumors and no tumors. The suggested model is trained and checked on BRATS datasets i.e., 2012(challenge and synthetic), 2013, and 2013 Leaderboard, 2014, and 2015 datasets. The presented model is evaluated with a number of performance metrics which demonstrates the improved performance.
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http://dx.doi.org/10.1007/s10916-019-1483-2DOI Listing
December 2019

Oral Rivaroxaban in Symptomatic Deep Vein Thrombosis.

J Coll Physicians Surg Pak 2019 Sep;29(9):814-818

Department of General Medicine, Pakistan Institute of Medical Sciences (PIMS), Islamabad, Pakistan.

Objective: To evaluate the efficacy of oral rivaroxaban compared to warfarin in patients with deep vein thrombosis (DVT).

Study Design: Open label randomized controlled study.

Place And Duration Of Study: Department of General Medicine, Pakistan Institute of Medical Sciences (PIMS), Islamabad from January 2016 to January 2018.

Methodology: Patients of both genders between 18 and 60 years of age with Doppler ultrasound-confirmed DVT were included in the study. Pregnant patients and those with advanced liver, renal disease, those with a previous history of DVT, malignancy, with a platelets count of less than 50000/ul were excluded from the study. Rivaroxaban was given in a dose of 15 mg twice daily for 21 days followed by 20 mg once daily. Patients in the warfarin group were given heparin for 3 to 5 days followed by warfarin for 3 to 6 months. The primary efficacy outcome was patency of the vessel at 3 and 6 months of treatment. The principal safety outcomes were major and minor bleeding during the study period.

Results: A total of 151 patients with acute symptomatic deep vein thrombosis were enrolled in the study. Half of the patients were given warfarin and the other half rivaroxaban for 6 months. At three months, there were no significant differences observed in vessel patency in the rivaroxaban group (22.4%) as compared to warfarin group (26.7%) but after 6 months of therapy, vessel patency was significantly more in the rivaroxaban group. Adverse events did not show any significant differences Conclusion: Rivaroxaban had an efficacy superior to warfarin in terms of vessel patency after six months of therapy but adverse events were similar in both the groups.
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http://dx.doi.org/10.29271/jcpsp.2019.09.814DOI Listing
September 2019

Brain tumor detection using statistical and machine learning method.

Comput Methods Programs Biomed 2019 Aug 17;177:69-79. Epub 2019 May 17.

College of EME, NUST, Islamabad, Pakistan.

Background And Objective: Brain tumor occurs because of anomalous development of cells. It is one of the major reasons of death in adults around the globe. Millions of deaths can be prevented through early detection of brain tumor. Earlier brain tumor detection using Magnetic Resonance Imaging (MRI) may increase patient's survival rate. In MRI, tumor is shown more clearly that helps in the process of further treatment. This work aims to detect tumor at an early phase.

Methods: In this manuscript, Weiner filter with different wavelet bands is used to de-noise and enhance the input slices. Subsets of tumor pixels are found with Potential Field (PF) clustering. Furthermore, global threshold and different mathematical morphology operations are used to isolate the tumor region in Fluid Attenuated Inversion Recovery (Flair) and T2 MRI. For accurate classification, Local Binary Pattern (LBP) and Gabor Wavelet Transform (GWT) features are fused.

Results: The proposed approach is evaluated in terms of peak signal to noise ratio (PSNR), mean squared error (MSE) and structured similarity index (SSIM) yielding results as 76.38, 0.037 and 0.98 on T2 and 76.2, 0.039 and 0.98 on Flair respectively. The segmentation results have been evaluated based on pixels, individual features and fused features. At pixels level, the comparison of proposed approach is done with ground truth slices and also validated in terms of foreground (FG) pixels, background (BG) pixels, error region (ER) and pixel quality (Q). The approach achieved 0.93 FG and 0.98 BG precision and 0.010 ER on a local dataset. On multimodal brain tumor segmentation challenge dataset BRATS 2013, 0.93 FG and 0.99 BG precision and 0.005 ER are acquired. Similarly on BRATS 2015, 0.97 FG and 0.98 BG precision and 0.015 ER are obtained. In terms of quality, the average Q value and deviation are 0.88 and 0.017. At the fused feature based level, specificity, sensitivity, accuracy, area under the curve (AUC) and dice similarity coefficient (DSC) are 1.00, 0.92, 0.93, 0.96 and 0.96 on BRATS 2013, 0.90, 1.00, 0.97, 0.98 and 0.98 on BRATS 2015 and 0.90, 0.91, 0.90, 0.77 and 0.95 on local dataset respectively.

Conclusion: The presented approach outperformed as compared to existing approaches.
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http://dx.doi.org/10.1016/j.cmpb.2019.05.015DOI Listing
August 2019

Diabetic retinopathy detection and classification using hybrid feature set.

Microsc Res Tech 2018 Sep;81(9):990-996

Department of Computer Science, COMSATS Institute of Information Technology, Vehari, Pakistan.

Complicated stages of diabetes are the major cause of Diabetic Retinopathy (DR) and no symptoms appear at the initial stage of DR. At the early stage diagnosis of DR, screening and treatment may reduce vision harm. In this work, an automated technique is applied for detection and classification of DR. A local contrast enhancement method is used on grayscale images to enhance the region of interest. An adaptive threshold method with mathematical morphology is used for the accurate lesions region segmentation. After that, the geometrical and statistical features are fused for better classification. The proposed method is validated on DIARETDB1, E-ophtha, Messidor, and local data sets with different metrics such as area under the curve (AUC) and accuracy (ACC).
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http://dx.doi.org/10.1002/jemt.23063DOI Listing
September 2018

Fundus image classification methods for the detection of glaucoma: A review.

Microsc Res Tech 2018 Oct 3;81(10):1105-1121. Epub 2018 Oct 3.

Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan.

Glaucoma is a neurodegenerative illness and is considered as a standout amongst the most widely recognized reasons for visual impairment. Nerve's degeneration is an irretrievable procedure, so the diagnosis of the illness at an early stage is an absolute requirement to stay away from lasting loss of vision. Glaucoma effected mainly because of increased intraocular pressure, if it is not distinguished and looked early, it can result in visual impairment. There are not generally evident side effects of glaucoma; thus, patients attempt to get treatment just when the seriousness of malady is advanced altogether. Determination of glaucoma often comprises of review of the basic crumbling of the nerve in conjunction with the examination of visual function capacity. This article shows the persistent illustration of glaucoma, its side effects, and the potential people inclined to this malady. The essence of this article is on different classification methods being utilized and proposed by various scientists for the identification of glaucoma. This article audits a few division and segmentation methodologies that are exceptionally useful for recognizable proof, identification, and diagnosis of glaucoma. The research related to the findings and the treatment is likewise evaluated in this article.
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http://dx.doi.org/10.1002/jemt.23094DOI Listing
October 2018
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