An Exploration of Deep Transfer Learning with a Layer-Modified AlexNet for Brain Tumor Classification

Brain is the primary organ of the neurological system in humans, brain tumors are among the most fatal conditions that affect the majority of people. Brain tumors can dispute brain functions and this is depending on their type, location and size can cause serious health issues. In modern medicine, MRI (Magnetic Resonance Imaging) stands out as the most effective imaging technique for diagnosing brain tumors because of its superior soft tissue resolution. But traditional diagnostic methods are often subjective, time-consuming, leading to delayed interventions and misdiagnoses to classify brain tumors. This study represents a modified 4 -layer AlexNet CNN model that classifies brain tumors into four categories with an accuracy of 98.75%. The model's efficacy is assessed using metrics including accuracy, F1-score, recall, and precision. Particularly in places with few medical resources, this strategy may greatly improve categorization outcomes and increase access to dermatological care.