Brain tumor classificaion using deep learning.

Abstract

Brain tumors are a common type of cancer that affects brain tissue. They often cause symp- toms such as headaches or seizures. They are usually diagnosed through brain scans such as magnetic resonance imaging (MRI). In recent years, computer scientists have developed algorithms that have shown promising results in automatically classifying these images into various types using deep learning models, which is a type of machine learning that uses artificial neural networks to recognize patterns in data. Publicly available MRI scans (1500 cancerous and 1500 non-cancerous) are used to train deep learning models: VGG16, VGG19, ResNet50, and Xception. Each model is implemented using three approaches, namely: implementation from scratch, transfer learning, and fine- tuning. This comparative study aims to find the best approach for training models on small datasets. The obtained overall accuracies ranged from 88% to 99%.