In brain-related diseases, including Brain Tumours and Alzheimer’s, accurate and timely diagnosis is crucial for effective medical intervention. Current state-of-the-art (SOTA) approaches in medical imaging predominantly focus on diagnosing a single brain disease at a time. However, recent research has uncovered intricate connections between various brain diseases, realizing that treating one condition may lead to the development of others. Consequently, there is a growing need for accurate diagnostic systems addressing multiple brain-related diseases. Designing separate models for different diseases, however, can impose substantial overhead. To tackle this challenge, our paper introduces \(\mathcal {B}\text {rain}{\mathcal{M}\mathcal{N}}\text {et}\) , an innovative neural network architecture explicitly tailored for classifying brain images. The primary objective is to propose a single, robust framework capable of diagnosing a spectrum of brain-related diseases. The paper comprehensively validates \(\mathcal {B}\text {rain}{\mathcal{M}\mathcal{N}}\text {et}\) ’s efficacy, specifically in diagnosing Brain tumours and Alzheimer’s disease. Remarkably, the proposed model workflow surpasses current SOTA methods, demonstrating a substantial enhancement in accuracy and precision. Furthermore, it maintains a balanced performance across different classes in the Brain tumour and Alzheimer’s dataset, emphasizing the versatility of our architecture for precise disease diagnosis. \(\mathcal {B}\text {rain}{\mathcal{M}\mathcal{N}}\text {et}\) undergoes an ablation study to optimize its choice of the optimal optimizer, and a data growth analysis verifies its performance on small datasets, simulating real-life scenarios where data progressively increase over time. Thus, this paper signifies a significant stride toward a unified solution for diagnosing diverse brain-related diseases.
