B2 Vertaisarvioimaton kirjan tai muun kokoomateoksen osa
Brain stroke detection from computed tomography images using deep learning algorithms
Tekijät: Diker Aykut, Elen Abdullah, Subasi Abdulhamit
Toimittaja: Subasi Abdulhamit
Kustantaja: Elsevier
Julkaisuvuosi: 2022
Kokoomateoksen nimi: Applications of Artificial Intelligence in Medical Imaging
Tietokannassa oleva lehden nimi: Applications of Artificial Intelligence in Medical Imaging
Sarjan nimi: Artificial Intelligence Applications in Healthcare & Medicine
Aloitussivu: 207
Lopetussivu: 222
ISBN: 978-0-443-18451-2
eISBN: 978-0-443-18450-5
DOI: https://doi.org/10.1016/B978-0-443-18450-5.00013-X
Verkko-osoite: https://www.sciencedirect.com/science/article/abs/pii/B978044318450500013X
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%.