Salsabila, Dea Amanda (2025) EXPLAINABLE AI (XAI) UNTUK DETEKSI PENYAKIT ALZHEIMER MENGGUNAKAN DATA PENCITRAAN MRI Explainable AI (XAI) for Alzheimer's Disease Detection Using MRI Imaging Data. Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.
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Abstract
Alzheimer's disease is a neurodegenerative disorder that causes a gradual decline in cognitive function. Early detection is crucial for slowing disease progression; however, MRI-based diagnostic processes still face challenges such as data imbalance and a lack of interpretability regarding AI system outputs. This study employs deep transfer learning methods using EfficientNetB0, VGG16, and VGG19 models, alongside two ensemble models: Ensemble 1 (EfficientNetB0 + VGG16) and Ensemble 2 (EfficientNetB0 + VGG19). To enhance result transparency, Explainable AI (XAI) techniques—specifically Grad-CAM, Saliency Map, and Guided Grad-CAM—are applied to visualize critical regions within the MRI images. Evaluation results indicate that the VGG16 model achieved the highest accuracy at 95.41%, followed by the two ensemble models, which also demonstrated high performance with accuracy exceeding 94%. Guided Grad-CAM visualizations help highlight medically relevant brain regions, ensuring the system is not only accurate but also interpretable, thereby supporting clinical diagnosis.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Uncontrolled Keywords: | Alzheimer's, Deep Transfer Learning, AI, Dementia, Impairment |
| Subjects: | Q Science > QM Human anatomy T Technology > T Technology (General) |
| Divisions: | Fakultas Teknik > Program Studi Teknik Informatika |
| Depositing User: | 1462100154 Dea Amanda Salsabila |
| Date Deposited: | 22 Jun 2026 02:48 |
| Last Modified: | 22 Jun 2026 02:48 |
| URI: | http://repository.untag-sby.ac.id/id/eprint/42866 |
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