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

被引：3

作者：

Azaharan, Tun Azshafarrah Ton Komar ^{[1
]}

Mahamad, Abd Kadir ^{[1
]}

Saon, Sharifah ^{[1
]}

Muladi, Sri Wiwoho ^{[2
]}

Mudjanarko, Sri Wiwoho ^{[3
]}

机构：

[1] Univ Tun Hussein Onn Malaysia, Fac Elect & Elect Engn, Johor Baharu, Malaysia

[2] Univ Negeri Malang, Dept Elect Engn, Malang, Indonesia

[3] Univ Narotama, Civil Engn, Surabaya, Indonesia

来源：

INTERNATIONAL JOURNAL OF ONLINE AND BIOMEDICAL ENGINEERING | 2023年 / 19卷 / 08期

关键词：

brain tumor; classification; Vgg-16; Reset; -50; AlexNet;

D O I：

10.3991/ijoe.v19i08.38619

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

brain tumours are extremely frequent and deadly, and if they are not found in their early stages, they can shorten a person's lifespan. After the tumour has been detected, it is essential to classify the tumour in order to develop a successful treatment strategy. 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 con-tained 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%. The early diagnosis of cancers before they develop physical side effects like paralysis and other problems is positively impacted by these accuracy.

引用

页码：97 / 109

页数：13

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