Enhanced ROI guided deep learning model for Alzheimer's detection using 3D MRI images

Abstract

Alzheimer's disease is an incurable condition that predominantly affects the human brain, leading to the shrinkage of various brain regions and the disruption of neuronal connections. Current state-of-the-art methods for detecting Alzheimer's disease using 3D MRI images are resource-intensive and time-consuming. In this paper, we propose a Regions of Interest (ROI)-guided detection paradigm to address these challenges. We employ a 3D ResNet integrated with a Convolutional Block Attention Module (CBAM), demonstrating that emphasising ROIs in brain imaging can substantially reduce both computational expenditure and training time. Our model exhibits robust performance in discriminating Alzheimer's disease from mild cognitive impairment, achieving an accuracy of 88% across the entire brain and 92% within targeted ROIs on the ADNI dataset.

Contributions

1. 01

   ROI-guided detection paradigm that reduces computational cost and training time for 3D MRI analysis.

2. 02

   3D ResNet integrated with Convolutional Block Attention Module (CBAM) for targeted brain region focus.

3. 03

   92% classification accuracy within targeted ROIs on the ADNI dataset, outperforming full-brain inference.

Headline results