Towards Explainable Detection of Alzheimer's Disease: A Fusion of Deep Convolutional Neural Network and Enhanced Weighted Fuzzy C-Mean.

BACKGROUND

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, posing a significant challenge for individuals and society. Early detection and treatment are essential for effective disease management.

OBJECTIVE

The objective of this research is to develop a novel and interpretable deep learning model for rapid and accurate Alzheimer's disease detection, incorporating Explainable Artificial Intelligence (XAI) techniques. The model aims to ensure generalizability through cross-validation and data augmentation, while enhancing interpretability and transparency by using Explainable Artificial Intelligence methods such as Grad-CAM, SHAP, and LIME, alongside an Enhanced Fuzzy C-Means (FCM) algorithm to clarify feature categorization and improve understanding of the model's decision-making process.

METHODS

The proposed model employs a multi-stage approach. Initially, MRI scans are transformed into feature vectors suitable for input into a Deep Convolutional Neural Network (CNN). Subsequently, an Enhanced Fuzzy C-Mean (FCM) algorithm, incorporating spatial information, refines these features to improve clustering precision. The model integrates Explainable Artificial Intelligence techniques, including Grad-CAM, SHAP, and LIME, to elucidate critical features and regions influencing classification outcomes. The performance metrics such as Accuracy, Recall and Specificity are used for assessing the performance of the model.

RESULTS

The XAI-DEF Alzheimer's disease detection model consistently demonstrated exceptional performance across both the ADNI and OASIS datasets. On ADNI, the model achieved an accuracy of 99.39%, recall of 99.47%, and specificity of 99.3%. Similarly, on OASIS, the model attained an accuracy of 99.36%, recall of 99.53%, and specificity of 99.15%. These results underscore the model's effectiveness in accurately classifying Alzheimer's disease cases while minimizing false positives and negatives.

CONCLUSION

Through the development of this model, we contribute to the advancement of dependable diagnostic tools tailored for the detection and management of Alzheimer's disease. By prioritizing interpretability alongside accuracy, our approach provides valuable insights into the decisionmaking process of the model, ultimately improving patient outcomes and facilitating further research in neurodegenerative disorders.