Robust Brain Tumor Classification using Augmented MRI Data and Deep CNNs

Treatment planning and patient outcomes can be greatly improved with early and precise brain tumor categorization. But there are challenges, such as a lack of annotated medical imaging data and the fact that tumors might have varying appearances. with deep convolutional neural networks (CNNs) trained with enhanced MRI data, this study presents a realistic way to classifying brain cancers. By using a variety of data augmentation techniques, the suggested strategy makes the training set more diverse and reduces the likelihood of overfitting. The use of intensity fluctuations, geometric modifications, and elastic deformations are all instances of such techniques. An architecture of a custom-built convolutional neural network (CNN) learns hierarchical characteristics automatically from MRI data, enabling reliable tumor type detection. Results for classification accuracy, sensitivity, and specificity were better for the model than for conventional CNNs and earlier machine learning approaches when evaluated on a benchmark brain MRI dataset. The results show that computer-assisted brain tumor diagnostic systems can boost their generalizability and reliability by combining data augmentation with deep learning.