AlexNet Architecture for Classifying of Magnetic Resonance Imaging Scans of the Brain

Classification of MRI brain images using AlexNet and deep learning algorithms is the goal of this study. The goal is to create a reliable and effective system that can classify Magnetic Resonance Imaging (MRI) scans into meaningful clinical categories, such as normal, benign, or cancerous. Improving the diagnosis of neurological illnesses and anomalies is the primary objective of using deep learning, namely the AlexNet architecture. This work aims to enhance medical decision-making and patient care by developing and validating a system that can reliably categorize brain images to high levels of accuracy. The end goal is to provide a flexible and extensible framework that can be included into existing clinical procedures; this will help medical personnel make faster diagnoses based on better MRI scan interpretation. Improving neuroimaging diagnostic tools and procedures is the ultimate goal of this study, which aims to expand the frontiers of medical image analysis. Results proved that the proposed AlexNet system achieves 95.0 % overall accuracy with 93.8% sensitivity and 99% specificity.