Investigation of VGG-16, ResNet-50 and AlexNet Performance for Brain Tumor Detection

Authors

  • Tun Azshafarrah Ton Komar Azaharan Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia
  • Abd Kadir Mahamad Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia https://orcid.org/0000-0002-3985-6549
  • Sharifah Saon Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia https://orcid.org/0000-0003-2706-3258
  • Muladi Universitas Negeri Malang, Indonesia https://orcid.org/0000-0002-2904-5398
  • Sri Wiwoho Mudjanarko Universitas Narotama, Surabaya, Indonesia

DOI:

https://doi.org/10.3991/ijoe.v19i08.38619

Keywords:

brain tumor, classification, VGG-16, ResNet-50, AlexNet

Abstract


A brain tumor is a very common and devastating malignant tumor that leads to a shorter lifespan if not detected early enough. Brain tumor classification is a critical step after the tumor has been identified to create an effective treatment plan. This study aims to investigate the three deep learning tools, VGG-16 ResNet50 and AlexNet in order to detect brain tumor using MRI images. The results performance are then evaluated and compared using accuracy, precision and recall criteria. The dataset used contained 155 MRI images which are images with tumors, and 98 of them are non-tumors. The AlexNet model perform extremely well on the dataset with 96.10% accuracy, while VGG-16 achieved 94.16% and ResNet-50 achieved 91.56%. These accuracies positively impact the early detection of tumors before the tumor causes physical side effects such as paralysis and other disabilities.

Cover iJOE Vol. 19 No. 08 (2023)

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Published

2023-06-27

How to Cite

Ton Komar Azaharan, T. A. ., Mahamad, A. K., Saon, S., Muladi, & Mudjanarko, S. W. . (2023). Investigation of VGG-16, ResNet-50 and AlexNet Performance for Brain Tumor Detection. International Journal of Online and Biomedical Engineering (iJOE), 19(08), pp. 97–109. https://doi.org/10.3991/ijoe.v19i08.38619

Issue

Vol. 19 No. 08 (2023)

Section

Papers

License

Copyright (c) 2023 Tun Azshafarrah Ton Komar Azaharan, Abd Kadir Mahamad, SHARIFAH SAON, Muladi Muladi , Sri Wiwoho Mudjanarko

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