Comparison of VGG-19 and RESNET-50 Algorithms in Brain Tumor Detection

Abstract

The brain is the organ that governs all of the body's functions. A brain tumor is a malignant or noncancerous development of aberrant cells and tissues in the brain. The average survival rate for people with primary brain tumors is 75.2 percent, thus early detection is critical. The identification of brain tumors is a crucial but time-consuming procedure. Traditional procedures are time-consuming and prone to human error. Computer-assisted diagnosis of brain cancers is unavoidable to overcome these constraints. Automated Brain Tumor Recognition from Magnetic Resonance Images could be a good answer to this problem.This study uses Deep Learning models to diagnose a brain tumor based on MRI scan results. The Brain tumor detection system analyzes MRI data using image processing and deep learning algorithms to detect cancers. This study compares the VGG19, and ResNet50 models for processing and detecting brain cancers based on their accuracy while using the same dataset.