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ORIGINAL RESEARCH article
Front. Comput. Neurosci.
Volume 18 - 2024 |
doi: 10.3389/fncom.2024.1360095
This article is part of the Research Topic Computational Modeling and Machine Learning Methods in Neurodevelopment and Neurodegeneration: from Basic Research to Clinical Applications View all 10 articles
Deep Learning-based Alzheimer's Disease detection: reproducibility and the effect of modeling choices
Provisionally accepted- University of Genoa, Genoa, Italy
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.We utilize Magnetic Resonance Imaging (MRI) data from the ADNI corpus to address a binary classification problem using 3D Convolutional Neural Networks (CNNs). Data processing and modeling are specifically tailored to address data scarcity and minimize computational overhead. Within this framework, we train 15 predictive models, considering three different data augmentation strategies and five distinct 3D CNN architectures with varying convolutional layers counts. The augmentation strategies involve affine transformations, such as zoom, shift, and rotation, applied either concurrently or separately.The combined effect of data augmentation and model complexity results in up to 10% variation in prediction accuracy. Notably, when affine transformation are applied separately, the model achieves higher accuracy, regardless the chosen architecture. Across all strategies, the model accuracy exhibits a concave behavior as the number of convolutional layers increases, peaking at an intermediate value. The best model reaches excellent performance both on the internal and additional external testing set. Discussions Our work underscores the critical importance of adhering to rigorous experimental practices in the field of ML applied to healthcare. The results clearly demonstrate how data augmentation and model depth -often overlooked factors-can dramatically impact final performance if not thoroughly investigated. This highlights both the necessity of exploring 1 Turrisi et al. DL-based AD detection: reproducibility and the effect of modeling choices neglected modeling aspects and the need to comprehensively report all modeling choices to ensure reproducibility and facilitate meaningful comparisons across studies.
Keywords: deep learning, Alzheimer's disease, Data augmentation, model depth, reproducibility
Received: 22 Dec 2023; Accepted: 03 Sep 2024.
Copyright: © 2024 Turrisi, Verri and Barla. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Rosanna Turrisi, University of Genoa, Genoa, Italy
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