Deep learning-based Alzheimer's disease detection: reproducibility and the effect of modeling choices.

ABSTRACT:

Introduction

Machine Learning (ML) has emerged as a promising approach in healthcare, outperforming traditional statistical techniques. However, to establish ML as a reliable tool in clinical practice, adherence to best practices in data handling, and modeling design and assessment is crucial. In this work, we summarize and strictly adhere to such practices to ensure reproducible and reliable ML. Specifically, we focus on Alzheimer's Disease (AD) detection, a challenging problem in healthcare. Additionally, we investigate the impact of modeling choices, including different data augmentation techniques and model complexity, on overall performance.

Methods

We utilize Magnetic Resonance Imaging (MRI) data from the ADNI corpus to address a binary classification pro

SUBMITTER: Turrisi R 

PROVIDER: S-EPMC11451303 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature