Classification of Brain Tumor with Deep Learning Models

Detecting brain tumors is crucial for timely treatment, given their life-threatening nature. Recent advancements in deep learning, particularly the use of Convolutional Neural Networks (CNNs), have significantly improved medical diagnostics in healthcare, especially in the identification of brain cancers through MRI images. To enhance the effectiveness of deep learning methods, particularly CNN s, especially with limited datasets, data augmentation is a well-established technique. This study conducts a comprehensive analysis of various CNN architectures, focusing on models such as VGG. The research introduces an effective method for detecting brain cancers in MRI datasets using CNN s and data augmentation. The proposed solution demonstrates success in advancing the field by improving deep architectural design and achieving high detection accuracy, as affirmed by evaluation metrics. Identifying brain tumors is crucial due to their potential fatality, and this study contributes to the ongoing progress in medical diagnostics by leveraging state-of-the-art deep learning techniques.