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The IUP Journal of Computer Sciences

Comparative Assessment of ResNet and VGG16 in Brain Tumor Classification

Article Details

Pub. Date	: Jan, 2024
Product Name	: The IUP Journal of Computer Sciences
Product Type	: Article
Product Code	: IJCS020124
Author Name	: Agnibh Pathak, Abir Dey and Vijayalakshmi A
Availability	: YES
Subject/Domain	: Management
Download Format	: PDF Format
No. of Pages	: 13

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Abstract

Brain tumor presents a complex challenge in modern medical treatment. During the initial stages of tumor growth, radiologists prioritize accurate and efficient analysis for which deep learning has emerged as a valuable tool. In particular, the deep residual network (ResNet), incorporating convolutional neural networks (CNNs) and VGG16, has demonstrated significant success in detecting and categorizing images of tumors. This advancement in deep learning holds the potential to aid radiologists in diagnosing tumors noninvasively. By enhancing comprehension of MRI images and improving training speed and accuracy, deep learning could revolutionize medical research. This study focuses on employing transfer learning with pretrained ResNet50 and VGG16 models to investigate multiclass brain tumor classification. The achieved accuracy rates are noteworthy: 78.32% for VGG16 and 80.10% for ResNet on brain tumor dataset.

Introduction

Identification of brain tumors is a crucial and intricate field of medical diagnostics that focuses on the process of finding abnormal growths in brain tissue. Powerful computer algorithms and machine learning techniques are integrated with advanced imaging techniques like magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) scans to perform various studies. Early and accurate diagnosis of brain tumors is critical for timely medical intervention and personalized treatment. As technology advances, the search for more precise and efficient methods of brain tumor identification remains a priority, with the potential to improve patient outcomes and significantly contribute to the broader landscape of neurology and oncology. Technological breakthroughs have significantly transformed the landscape of brain tumor detection, revolutionizing the accuracy, speed and noninvasive nature of the