Towards Practical Application of Deep Learning in Diagnosis of Alzheimer's Disease

TL;DR

This study demonstrates the effective application of 3D deep learning models for diagnosing Alzheimer's disease from full brain MRI scans, achieving high accuracy and extracting meaningful features aligned with clinical markers.

Contribution

It introduces 3D versions of well-known 2D CNNs for AD diagnosis, showing improved performance and feature interpretability over traditional methods.

Findings

3D CNNs achieved high classification accuracy on AD stages.

Ensemble models outperformed individual CNNs.

Extracted features aligned with anatomical landmarks important for AD identification.

Abstract

Accurate diagnosis of Alzheimer's disease (AD) is both challenging and time consuming. With a systematic approach for early detection and diagnosis of AD, steps can be taken towards the treatment and prevention of the disease. This study explores the practical application of deep learning models for diagnosis of AD. Due to computational complexity, large training times and limited availability of labelled dataset, a 3D full brain CNN (convolutional neural network) is not commonly used, and researchers often prefer 2D CNN variants. In this study, full brain 3D version of well-known 2D CNNs were designed, trained and tested for diagnosis of various stages of AD. Deep learning approach shows good performance in differentiating various stages of AD for more than 1500 full brain volumes. Along with classification, the deep learning model is capable of extracting features which are key in differentiating the various categories. The extracted features align with meaningful anatomical landmarks, that are currently considered important in identification of AD by experts. An ensemble of all the algorithm was also tested and the performance of the ensemble algorithm was superior to any individual algorithm, further improving diagnosis ability. The 3D versions of the trained CNNs and their ensemble have the potential to be incorporated in software packages that can be used by physicians/radiologists to assist them in better diagnosis of AD.