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All Journal International Journal of Electronics and Communications Systems
Abdalla, Peshraw Ahmed
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Computer Science & IT Electrical & Electronics Engineering

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Journal : International Journal of Electronics and Communications Systems

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Deep Transfer Learning Networks for Brain Tumor Detection: The Effect of MRI Patient Image Augmentation Methods Abdalla, Peshraw Ahmed; Qadir, Abdalbasit Mohammed; Rashid, Omed Jamal; Rawf, Karwan M. Hama; Abdulrahman, Ayub O; Mohammed, Bashdar Abdalrahman
International Journal of Electronics and Communications Systems Vol. 2 No. 2 (2022): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v2i2.14815

Abstract

The exponential growth of deep learning networks has enabled us to handle difficult tasks, even in the complex field of medicine with small datasets. In the sphere of treatment, they are particularly significant. To identify brain tumors, this research examines how three deep learning networks are affected by conventional data augmentation methods, including MobileNetV2, VGG19, and DenseNet201. The findings showed that before and after utilizing approaches, picture augmentation schemes significantly affected the networks. The accuracy of MobileNetV2, which was originally 85.33 percent, was then enhanced to 96.88 percent. The accuracy of VGG19, which was 77.33 percent, was then enhanced to 95.31 percent, and DenseNet201, which was originally 82.66 percent, was then enhanced to 93.75 percent. The models' accuracy percentage engagement change is 13.53 percent, 23.25 percent, and 23.25 percent, respectively. Finally, the conclusion showed that applying data augmentation approaches improves performance, producing models far better than those trained from scratch.
Co-Authors Abdulrahman, Ayub O Mohammed, Bashdar Abdalrahman Qadir, Abdalbasit Mohammed Rashid, Omed Jamal Rawf, Karwan M. Hama