Deep CNN Model for Classifying Neurological Diseases using MRI Brain Images

This study investigates the effectiveness of a deep learning model developed for the diagnosis of multiple neurological diseases. Using MRI brain images, the classification of neurological disorders such as Acute Ischemic Stroke, Very Mild Dementia, Glioma, Mild Dementia, Pituitary Tumor, Meningioma, Moderate Dementia, and Parkinson’s Disease was targeted. In our approach, data augmentation and balancing techniques were employed to enhance the accuracy of the model. The proposed CBMDF-Net model was evaluated in comparison with several pre-trained models, achieving an accuracy rate of 98.31%. These results demonstrate that our model performs exceptionally well in diagnosing neurological diseases. The developed model not only makes a significant contribution to the existing literature but also provides a promising foundation that could guide future studies.