Brain Tumor Diagnosis and Classification via Pre-Trained Convolutional Neural Networks

TL;DR

This paper explores the use of pre-trained convolutional neural networks to automate brain tumor diagnosis and classification from MRI images, aiming to improve accuracy and reduce manual errors.

Contribution

It introduces a method utilizing pretrained CNNs, specifically EfficientNet variants, for brain tumor detection and classification, demonstrating promising accuracy results.

Findings

EfficientNetB1 achieved 87.67% training accuracy.

EfficientNetB1 achieved 89.55% validation accuracy.

Pretrained CNNs outperform traditional manual diagnosis methods.

Abstract

The brain tumor is the most aggressive kind of tumor and can cause low life expectancy if diagnosed at the later stages. Manual identification of brain tumors is tedious and prone to errors. Misdiagnosis can lead to false treatment and thus reduce the chances of survival for the patient. Medical resonance imaging (MRI) is the conventional method used to diagnose brain tumors and their types. This paper attempts to eliminate the manual process from the diagnosis process and use machine learning instead. We proposed the use of pretrained convolutional neural networks (CNN) for the diagnosis and classification of brain tumors. Three types of tumors were classified with one class of non-tumor MRI images. Networks that has been used are ResNet50, EfficientNetB1, EfficientNetB7, EfficientNetV2B1. EfficientNet has shown promising results due to its scalable nature. EfficientNetB1 showed the best results with training and validation accuracy of 87.67% and 89.55%, respectively.