Brain MRI Classification Using Convolutional Neural Networks and VGG19: A Deep Learning Approach for Accurate Brain Disease Diagnosis

Brain MRI classification is a critical task in medical imaging analysis for accurate diagnosis of brain diseases. This study presents a deep learning approach utilizing Convolutional Neural Networks (CNNs) and the VGG19 architecture to achieve precise brain disease diagnosis based on brain MRI scans. The proposed methodology leverages the power of CNNs and transfer learning, employing the pre-trained weights of the VGG19 model. The depth and capability of the VGG19 architecture enable it to extract relevant features from brain MRI scans effectively. The process begins with data preparation and preprocessing to ensure dataset quality and consistency. The VGG19 model is adapted for brain MRI classification by replacing the final layers with task-specific layers. Transfer learning is applied by freezing initial layer weights and fine-tuning the remaining layers on the brain MRI dataset. The training process involves optimization using suitable algorithms and loss functions, with performance monitoring on a validation set. Hyperparameters are fine-tuned to achieve optimal results. The trained model is evaluated on an independent testing set to assess its diagnostic accuracy, utilizing metrics such as accuracy, precision, recall, and F1-score. The proposed deep learning approach demonstrates great potential for accurate brain disease diagnosis based on brain MRI scans. By automating the classification process, it reduces reliance on manual interpretation and enables faster, objective diagnoses. This study contributes to the advancement of medical imaging analysis and emphasizes the significance of deep learning techniques in brain MRI classification.