Transfer deep learning and explainable AI framework for brain tumor and Alzheimer's detection across multiple datasets.

Introduction: The pressing need for accurate diagnostic tools in the medical field, particularly for diseases such as brain tumors and Alzheimer's, poses significant challenges to timely and effective treatment. Methods: This study presents a novel approach to MRI image classification by integrating transfer learning with Explainable AI (XAI) techniques. The proposed method utilizes a hybrid CNN-VGG16 model, which leverages pre-trained features from the VGG16 architecture to enhance classification performance across three distinct MRI datasets: brain tumor classification, Alzheimer's disease detection, and a third dataset of brain tumors. A comprehensive preprocessing pipeline ensures optimal input quality and variability, including image normalization, resizing, and data augmentation. Results: The model achieves accuracy rates of 94% on the brain tumor dataset, 81% on the augmented Alzheimer dataset, and 93% on the third dataset, underscoring its capability to differentiate various neurological conditions. Furthermore, the integration of SHapley Additive exPlanations (SHAP) provides a transparent view of the model's decision-making process, allowing clinicians to understand which regions of the MRI scans contribute to the classification outcomes. Discussion: This research demonstrates the potential of combining advanced deep learning techniques with explainability to improve diagnostic accuracy and trust in AI applications within healthcare.