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

TL;DR

DEFORMISE is a deep learning framework that improves dementia diagnosis accuracy by selecting optimal MRI slices and utilizing a confidence-based classification system, validated on multiple datasets.

Contribution

The paper introduces a novel MRI slice selection technique combined with a confidence-based ensemble model for improved dementia diagnosis accuracy.

Findings

Achieved 94.12% accuracy on Open OASIS dataset

Validated robustness on ADNI dataset

Enhanced interpretability with explainable AI techniques

Abstract

Dementia, a debilitating neurological condition affecting millions worldwide, presents significant diagnostic challenges. In this work, we introduce DEFORMISE, a novel DEep learning Framework for dementia diagnOsis of eldeRly patients using 3D brain Magnetic resonance Imaging (MRI) scans with Optimized Slice sElection. Our approach features a unique technique for selectively 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. Furthermore, validation on the ADNI dataset confirmed the robustness and generalizability 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 advancement in dementia diagnosis, providing a highly accurate and efficient tool for clinical applications.