期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2019
卷号：10
期号：8
页码：459-465
出版社：Science and Information Society (SAI)
摘要：Computed Tomography (CT) imaging is one of the
conventional tools used to diagnose ischemic in Posterior Fossa
(PF). Radiologist commonly diagnoses ischemic in PF through
CT imaging manually. However, such a procedure could be
strenuous and time consuming for large scale images, depending
on the expertise and ischemic visibility. With the rapid
development of computer technology, automatic image
classification based on Machine Learning (ML) is widely been
developed as a second opinion to the ischemic diagnosis. The
practical performance of ML is challenged by the emergence of
deep learning applications in healthcare. In this study, we
evaluate the performance of deep transfer learning models of
Convolutional Neural Network (CNN); VGG-16, GoogleNet and
ResNet-50 to classify the normal and abnormal (ischemic) brain
CT images of PF. This is the first study that intensively studies
the application of deep transfer learning for automated ischemic
classification in the posterior part of brain CT images. The
experimental results show that ResNet-50 is capable to achieve
the highest accuracy performance in comparison to other
proposed models. Overall, this automatic classification provides a
convenient and time-saving tool for improving medical diagnosis.
