Automatic Assessment of Alzheimer's Disease Diagnosis Based on Deep Learning Techniques

TL;DR

This study demonstrates that deep learning models can effectively detect early Alzheimer's disease using sagittal MRI images, which are less commonly used but provide comparable accuracy to traditional methods, especially when employing transfer learning.

Contribution

The paper introduces the use of sagittal MRI images for Alzheimer's detection and shows that deep learning with transfer learning achieves results comparable to state-of-the-art methods using different MRI planes.

Findings

Sagittal MRI can distinguish AD-related damages.

Deep learning models with sagittal MRI perform similarly to horizontal-plane MRI models.

Transfer learning enables effective AD detection with fewer data.

Abstract

Early detection is crucial to prevent the progression of Alzheimer's disease (AD). Thus, specialists can begin preventive treatment as soon as possible. They demand fast and precise assessment in the diagnosis of AD in the earliest and hardest to detect stages. The main objective of this work is to develop a system that automatically detects the presence of the disease in sagittal magnetic resonance images (MRI), which are not generally used. Sagittal MRIs from ADNI and OASIS data sets were employed. Experiments were conducted using Transfer Learning (TL) techniques in order to achieve more accurate results. There are two main conclusions to be drawn from this work: first, the damages related to AD and its stages can be distinguished in sagittal MRI and, second, the results obtained using DL models with sagittal MRIs are similar to the state-of-the-art, which uses the horizontal-plane MRI. Although sagittal-plane MRIs are not commonly used, this work proved that they were, at least, as effective as MRI from other planes at identifying AD in early stages. This could pave the way for further research. Finally, one should bear in mind that in certain fields, obtaining the examples for a data set can be very expensive. This study proved that DL models could be built in these fields, whereas TL is an essential tool for completing the task with fewer examples.