Muhd Suberi, Anis Azwani and Wan Zakaria, Wan Nurshazwani and Tomari, Razali and Nazari, Ain and Mohd, Mohd Norzali and Nik Fuad, Nik Farhan (2019) Deep transfer learning application for automated ischemic classification in posterior fossa CT images. International Journal of Advanced Computer Science and Applications, 10 (8). pp. 459-465. (In Press)
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Abstract
Abstract—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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Deep learning; ischemic stroke; posterior fossa; classification; convolutional neural network; computed tomography; medical diagnosis. |
| Subjects: | R Medicine > RC Internal medicine |
| Divisions: | Faculty of Electrical and Electronic Engineering > Department of Electronic Enngineering |
| Depositing User: | Mr. Abdul Rahim Mat Radzuan |
| Date Deposited: | 05 Aug 2021 03:52 |
| Last Modified: | 05 Aug 2021 03:52 |
| URI: | http://eprints.uthm.edu.my/id/eprint/583 |
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