Brain Tumor Segmentation and Classification using Optimized Deep Learning

Abstract

In the medical field, identifying brain tumors is a complex task that necessitates specialists meticulously analyzing MRI scans to detect tumors. Recent advancements have introduced various artificial intelligence methods aimed at automating this process. However, prior approaches often relied on singular datasets, which constrained their ability to identify brain cancers across diverse scenarios. This study addresses this issue by employing data augmentation and denoising algorithms on medical images from three distinct datasets, aiming to enhance detection efficiency. To evaluate the effectiveness of these methods, we implemented two deep learning algorithms utilizing Convolutional Neural Networks (CNNs), which demonstrated high accuracy. These results suggest that incorporating data augmentation and denoising techniques can significantly improve the accuracy of brain tumor diagnoses. This research contributes to ongoing efforts in the medical field to refine the application of advanced machine learning techniques for the early and precise detection of brain cancers.