A convolutional neural network framework for automated brain disease detection using MRI

The human brain, a critical organ within the central nervous system, is vulnerable to a range of complex and life-threatening disorders, including brain tumors, Alzheimer’s disease, and stroke. Accurate and timely diagnosis of these conditions is essential for effective treatment and management. Traditionally, brain disease detection relies on manual interpretation of medical imaging modalities such as magnetic resonance imaging (MRI), a process that is time-intensive, prone to human error, and often lacks consistency. To address these limitations, this study proposes an automated deep learning-based framework for brain disease classification using the concept of transfer learning. A comparative analysis of four advanced convolutional neural network (CNN) architectures, VGG-16, VGG-19, EfficientNet, and DenseNet121 was conducted to evaluate their diagnostic performance on a publicly available MRI dataset. To enhance generalization and prevent overfitting, data augmentation techniques were applied during the training phase. The proposed pipeline comprised data acquisition, preprocessing, and comprehensive model evaluation stratified into various training and testing splits. Performance was rigorously assessed using metrics including accuracy, precision, recall, specificity, and F1-score. The results demonstrate that the VGG-16-based approach surpassed the other state-of-the-art models in classification performance, showcasing its potential as a reliable tool for automated brain disease diagnosis. This work underscores the applicability of deep learning in neuroimaging analysis and opens avenues for future improvements with more advanced architectures and multimodal data integration.