Open Access

ARTICLE

Deep Learning-Enabled Brain Stroke Classification on Computed Tomography Images

Azhar Tursynova¹, Batyrkhan Omarov^1,2, Natalya Tukenova^3,*, Indira Salgozha⁴, Onergul Khaaval³, Rinat Ramazanov⁵, Bagdat Ospanov⁵

1 Al-Farabi Kazakh National University, Almaty, Kazakhstan
2 International University of Tourism and Hospitality, Turkistan, Kazakhstan
3 Zhetisu University Named After I. Zhansugurov, Taldykorgan, Kazakhstan
4 Abai Kazakh National Pedagogical University, Almaty, Kazakhstan
5 Brunch Center of Excellence in Taldykorgan, Taldykorgan, Kazakhstan

* Corresponding Author: Natalya Tukenova. Email:

Computers, Materials & Continua 2023, 75(1), 1431-1446. https://doi.org/10.32604/cmc.2023.034400

Received 16 July 2022; Accepted 04 December 2022; Issue published 06 February 2023

Abstract

In the field of stroke imaging, deep learning (DL) has enormous untapped potential. When clinically significant symptoms of a cerebral stroke are detected, it is crucial to make an urgent diagnosis using available imaging techniques such as computed tomography (CT) scans. The purpose of this work is to classify brain CT images as normal, surviving ischemia or cerebral hemorrhage based on the convolutional neural network (CNN) model. In this study, we propose a computer-aided diagnostic system (CAD) for categorizing cerebral strokes using computed tomography images. Horizontal flip data magnification techniques were used to obtain more accurate categorization. Image Data Generator to magnify the image in real time and apply any random transformations to each training image. An early stopping method to avoid overtraining. As a result, the proposed methods improved several estimation parameters such as accuracy and recall, compared to other machine learning methods. A python web application was created to demonstrate the results of CNN model classification using cloud development techniques. In our case, the model correctly identified the drawing class as normal with 79% accuracy. Based on the collected results, it was determined that the presented automated diagnostic system could be used to assist medical professionals in detecting and classifying brain strokes.

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Keywords

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