Introducing DEFORMISE: a deep learning framework for dementia diagnosis in the elderly using optimized MRI slice selection

Dementia, a debilitating neurological condition affecting millions worldwide, presents significant diagnostic challenges. In this work, we introduce DE-FORMISE, a novel DEep learning Framework for dementia diagnOsis of el-deRly patients using 3D brain Magnetic resonance Imaging (MRI) scans with Optimised Slice sElection. Our approach features a unique technique for se-lectively processing MRI slices, focusing on the most relevant brain regions and excluding less informative sections. This methodology is complemented by a confidence-based classification committee composed of three novel deep learning models. Tested on the Open OASIS datasets, our method achieved an impressive accuracy of 94.12%, surpassing existing methodologies. Fur-thermore, validation on the ADNI dataset confirmed the robustness and gen-eralizability of our approach. The use of explainable AI (XAI) techniques and comprehensive ablation studies further substantiate the effectiveness of our techniques, providing insights into the decision-making process and the importance of our methodology. This research offers a significant advance-ment in dementia diagnosis, providing a highly accurate and efficient tool for clinical applications.