An Explainable and Class-Balanced Deep Learning Framework for Multi-Class Alzheimer's Disease Detection using MRI Scans
10th International Conference on Computer Science and Engineering, UBMK 2025, İstanbul, Türkiye, 17 - 21 Eylül 2025, ss.935-940, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/ubmk67458.2025.11206828
- Basıldığı Şehir: İstanbul
- Basıldığı Ülke: Türkiye
- Sayfa Sayıları: ss.935-940
- Anahtar Kelimeler: Alzheimer's Disease, Deep Learning, Explainable AI, SMOTETomek
- TED Üniversitesi Adresli: Evet
Özet
Alzheimer's Disease (AD) is one of the most common neurodegenerative diseases and early diagnosis is essential for decelerating its advancement. A Deep Learning (DL) based architecture is introduced for the classification o f A D stages using brain MRI scans. Three different models Custom Convolutional Neural Network (CNN), VGG16, and ResNet50 were implemented and compared in terms of accuracy, precision, recall, and F1-score. To handle class imbalance, SMOTETomek was applied to resample the dataset. Among the models, the VGG16-based transfer learning model delivered the highest accuracy of 0.98. Grad-CAM, a visual explainability technique, was used to interpret the VGG16 model's decisions. The heatmaps generated by Grad-CAM highlighted brain regions consistent with medical knowledge, supporting the reliability of model predictions. This study shows that combining DL with explainable artificial i n telligence ( X AI) m e thods c a n support more accurate and trustworthy AD diagnosis.