Automated Classification of Brain MRI Images Using Convolutional Neural Networks for Enhanced Tumor Detection

In this work, the application of CNNs to the classification of brain MRI images is investigated, with particular emphasis on the ability of these models to automatically extract characteristics and discriminate between a range of pathologies, including diseases associated with brain tumours. CNNs have several benefits, one of which is their ability to work directly with raw MRI data, eliminating the need for labour-intensive, manual feature extraction. Because magnetic resonance imaging (MRI) generates high-resolution images of brain regions, it is indispensable for the identification of neurological illnesses. However, manually interpreting MRI scans can be time-consuming and prone to human error, especially when small anomalies are visible. This work uses Convolutional neural networks (CNNs), which have shown considerable promise in medical image analysis due to their ability to learn complex characteristics straight from raw data, to automate the classification of brain MRI images. Consequently, this work shows how CNNs can greatly improve the efficacy and accuracy of brain MRI categorization, which will ultimately improve clinical workflows for diagnosis and patient outcomes.