B2 Non-refereed book chapter or chapter in a compilation book

Brain stroke detection from computed tomography images using deep learning algorithms




AuthorsDiker Aykut, Elen Abdullah, Subasi Abdulhamit

EditorsSubasi Abdulhamit

PublisherElsevier

Publication year2022

Book title Applications of Artificial Intelligence in Medical Imaging

Journal name in sourceApplications of Artificial Intelligence in Medical Imaging

Series titleArtificial Intelligence Applications in Healthcare & Medicine

First page 207

Last page222

ISBN978-0-443-18451-2

eISBN978-0-443-18450-5

DOIhttps://doi.org/10.1016/B978-0-443-18450-5.00013-X

Web address https://www.sciencedirect.com/science/article/abs/pii/B978044318450500013X


Abstract

Stroke is one of the common causes of death worldwide. Stroke is the inability of a focus to be fed in the brain due to clogged or bleeding of the vessels feeding the brain. Because early stroke treatment and diagnosis are related to a favorable patient outcome, time is a critical aspect of successful stroke treatment. In this chapter, we examine the stroke classification from Brain Stroke CT Dataset, with deep learning architectures. In the experimental study, a total of 2501 brain stroke computed tomography (CT) images were used for testing and training. For this purpose, numerus widely known pretrained convolutional neural networks (CNNs) such as GoogleNet, AlexNet, VGG-16, VGG-19, and Residual CNN were used to classify brain stroke CT images as normal and as stroke. Several performance metrics such as accuracy (ACC), specificity (SPE), sensitivity (SEN), and F-score are used to evaluate the performances of the classifier. The best classification results are achieved by VGG-19 with ACC 97.06%, SEN 97.41%, SPE 96.49%, and F-score 96.95%.



Last updated on 2024-26-11 at 17:33