A CNN-based Pipeline using Plane-wise Ensemble Technique for Classifying Alzheimer’s Disease from 3D MRI Images

Abstract

Alzheimer’s disease (AD) is a chronic neurodegenerative condition that progressively damages brain cells, resulting in memory and cognitive decline and eventually imped ing basic functionalities. With over 55 million people worldwide affected by demen tia, a number anticipated to rise significantly, the urgency for early diagnosis becomes paramount. While a definitive cure remains elusive, early intervention is crucial in mitigating disease progression and enhancing patient outcomes. This research inves tigates the potential of deep learning models for classifying Alzheimer’s Disease, em phasizing the challenges in Mild Cognitive Impairment (MCI) classification, and in troduces a CNN-based pipeline utilizing a plane-wise ensemble technique for 3D MRI image classification. To manage the complex nature of 3D MRI data, a CNN-based pipeline is proposed that makes use of a plane-wise ensemble technique. Decompos ing the 3D image into axial, coronal, and sagittal planes, and using an ensemble of 2D CNN models trained on the axial, coronal, and sagittal planes, the system attempts to include multi-view data and improve classification accuracy. This methodology also leverages projector functions to map the 3D volumes into a series of 2D images and tackles the computational challenges presented by 3D data, resulting in a more efficient and practical process even with constrained computation resources.