Ahmed Yahya Al-Galal, Sabaa and Taha Alshaikhli, Imad Fakhri and Abdulrazzaq, M. M. and Hassan, Raini (2021) Brain tumor MRI medical images classification with data augmentation by transfer learning of VGG16. Journal of Engineering Science and Technology (JESTEC), Special Issue (6/2021). pp. 21-32. ISSN 1823-4690
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Abstract
The ability to estimate conclusions without direct human input in healthcare systems via computer algorithms is known as Artificial intelligence (AI) in healthcare. Deep learning (DL) approaches are already being employed or exploited for healthcare purposes, and in the case of medical images analysis, DL paradigms opened a world of opportunities. This paper describes creating a DL model based on transfer learning of VGG16 that can correctly classify MRI images as either (tumorous) or (non-tumorous). In addition, the model employed data augmentation in order to balance the dataset and increase the number of images. The dataset comes from the brain tumour classification project, which contains publicly available tumorous and non-tumorous images. The result showed that the model performed better with the augmented dataset, with its validation accuracy reaching ~100 %.
| Item Type: | Article (Journal) |
|---|---|
| Uncontrolled Keywords: | A brain tumour, Classification, Medical images, MRI, Transfer learning, VGG16. |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): | Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science |
| Depositing User: | Dr. Raini Hassan |
| Date Deposited: | 27 Dec 2021 11:55 |
| Last Modified: | 27 Dec 2021 11:55 |
| URI: | http://irep.iium.edu.my/id/eprint/95278 |
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