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Artificial Intelligence in Medicine:Real Time Electronic Stethoscope for Heart Diseases Detection

Computers, Materials & Continua 2022年第2期70卷 2815-2833页

作者： Batyrkhan Omarov Nurbek Saparkhojayev Shyrynkyz Shekerbekova Oxana Akhmetova Meruert Sakypbekova Guldina Kamalova Zhanna Alimzhanova lyailya tukenova Zhadyra Akanova Al-Farabi Kazakh National University AlmatyKazakhstan Khoja Akhmet Yassawi International Kazakh-Turkish University TurkistanKazakhstan Abai Kazakh National Pedagogical University Kazakhstan NARXOZ University AlmatyKazakhstan

Diseases of the cardiovascular system are one of the major causes of death *** diseases could be quickly detected by changes in the sound created by the action of the *** dynamic auscultations need extensive professional knowledge and emphasis on listening *** is also an unmet requirement for a compact cardiac condition early warning *** this paper,we propose a prototype of a digital stethoscopic system for the diagnosis of cardiac abnormalities in real time using machine learning *** system consists of three subsystems that interact with each other(1)a portable digital subsystem of an electronic stethoscope,(2)a decision-making subsystem,and(3)a subsystemfor displaying and visualizing the results in an understandable *** electronic stethoscope captures the patient’s phonocardiographic sounds,filters and digitizes them,and then sends the resulting phonocardiographic sounds to the decision-making *** decision-making systemclassifies sounds into normal and abnormal using machine learning techniques,and as a result identifies abnormal heart *** display and visualization subsystem demonstrates the results obtained in an understandable way not only for medical staff,but also for patients and recommends further actions to *** a result of the study,we obtained an electronic stethoscope that can diagnose cardiac abnormalities with an accuracy of more than 90%.More accurately,the proposed stethoscope can identify normal heart sounds with 93.5%accuracy,abnormal heart sounds with 93.25%***,speed is the key benefit of the proposed stethoscope as 15 s is adequate for examination.

关键词： Stethoscope phonocardiogram machine learning classification heart diseases PCG

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Deep Learning-Enabled Brain Stroke Classification on Computed Tomography Images

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Computers, Materials & Continua 2023年第4期75卷 1431-1446页

作者： Azhar Tursynova Batyrkhan Omarov Natalya tukenova Indira Salgozha Onergul Khaaval Rinat Ramazanov Bagdat Ospanov Al-Farabi Kazakh National University AlmatyKazakhstan International University of Tourism and Hospitality TurkistanKazakhstan Zhetisu University Named After I.Zhansugurov TaldykorganKazakhstan Abai Kazakh National Pedagogical University AlmatyKazakhstan Brunch Center of Excellence in Taldykorgan TaldykorganKazakhstan

In the field of stroke imaging, deep learning (DL) has enormousuntapped *** clinically significant symptoms of a cerebral strokeare detected, it is crucial to make an urgent diagnosis using available imagingtechniques such as computed tomography (CT) scans. The purpose of thiswork is to classify brain CT images as normal, surviving ischemia or cerebralhemorrhage based on the convolutional neural network (CNN) model. In thisstudy, we propose a computer-aided diagnostic system (CAD) for categorizingcerebral strokes using computed tomography images. Horizontal flip datamagnification techniques were used to obtain more accurate *** Data Generator to magnify the image in real time and apply anyrandom transformations to each training image. An early stopping method toavoid overtraining. As a result, the proposed methods improved several estimationparameters such as accuracy and recall, compared to other machinelearning methods. A python web application was created to demonstrate theresults of CNN model classification using cloud development techniques. Inour case, the model correctly identified the drawing class as normal with 79%accuracy. Based on the collected results, it was determined that the presentedautomated diagnostic system could be used to assist medical professionals indetecting and classifying brain strokes.

关键词： Ischemic stroke hemorrhage CNN deep learning classification

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