{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T05:46:32Z","timestamp":1773380792620,"version":"3.50.1"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"9","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>Alzheimer\u2019s Disease (AD) is a progressive neurodegenerative disorder and a leading cause of dementia worldwide, making early and accurate diagnosis essential for effective clinical intervention. Although recent Deep Learning (DL) approaches have shown promising results for automated AD diagnosis from brain Magnetic Resonance Imaging (MRI), many existing methods remain limited by insufficient domain adaptation of pretrained models, severe class imbalance in clinical datasets, and suboptimal classification performance, which restrict their generalisability and clinical reliability. To address these challenges, this paper proposes a robust DL framework for multi-class classification of AD stages from structural MRI scans. The proposed framework is built upon a fine-tuned VGG19 model, enabling end-to-end learning and effective domain adaptation from natural images to medical imaging data, followed by a Multi-Layer Perceptron (MLP) for accurate stage-level classification. To mitigate the inherent class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied at the image level to balance class distributions during training. The framework is evaluated on OASIS dataset, publicly available via Kaggle, comprising 6,400 MRI images categorised into four classes. All MRI scans undergo a standardised preprocessing pipeline including image resizing, intensity normalisation, grayscale-to-RGB conversion, and training data augmentation. The dataset is split into training (80%) and testing (20%) sets using a stratified strategy. In addition, the proposed model is independently validated on ADNI dataset to assess robustness and cross-dataset generalisability. Experimental results demonstrate the effectiveness of the proposed framework, achieving an accuracy of 98.83% on OASIS and 99.13% on ADNI, along with high precision, recall, F1-score, and AUC, outperforming state-of-the-art methods.<\/jats:p>","DOI":"10.31449\/inf.v50i9.8187","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T22:21:30Z","timestamp":1773354090000},"source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning-Based Multi-Classification of Alzheimer\u2019s Disease Stages Using Fine-Tuned VGG19 and MLP on MRI Scans"],"prefix":"10.31449","volume":"50","author":[{"given":"Siham","family":"Amrouch","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ryma","family":"Guefrouchi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Islam","family":"Chabbi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,3,12]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/8187\/6550","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/8187\/6550","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T22:21:31Z","timestamp":1773354091000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/8187"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,12]]},"references-count":0,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,3,12]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i9.8187","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,12]]}}}