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Generalization Performance of the Deep Learning Models in Neurodegenerative Disease Classification.

Yagis, Ekin and Garcia Seco De Herrera, Alba and Citi, Luca (2019) Generalization Performance of the Deep Learning Models in Neurodegenerative Disease Classification. In: 10th International Workshop on Biomedical and Health Informatics (BHI), 2019-11-18 - 2019-11-20, San Diego, CA, USA.

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Abstract

Over the past decade, machine learning gained considerable attention from the scientific community and has progressed rapidly as a result. Given its ability to detect subtle and complicated patterns, deep learning (DL) has been utilized widely in neuroimaging studies for medical data analysis and automated diagnostics with varying degrees of success. In this paper, we question the remarkable accuracies of the best performing models by assessing generalization performance of the stateof-the-art convolutional neural network (CNN) models on the classification of two most common neurodegenerative diseases, namely Alzheimer’s Disease (AD) and Parkinson’s Disease (PD) using MRI. We demonstrate the impact of the data division strategy on the model performances by comparing the results derived from two different split approaches. We first evaluated the performance of the CNN models by dividing the dataset at the subject level in which all of the MRI slices of a patient are put into either training or test set. We then observed that pooling together all slices prior to applying cross-validation, as erroneously done in a number of previous studies, leads to inflated accuracies by as much as 26% for the classification of the diseases.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: Proceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
Uncontrolled Keywords: Parkinson’s Disease; Alzheimer’s Disease; Deep Learning; Transfer Learning; VGG16; Resnet50; MRI; Neuroimaging
Divisions: Faculty of Science and Health
Faculty of Social Sciences
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Faculty of Social Sciences > Institute for Social and Economic Research
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 27 Jan 2020 11:08
Last Modified: 23 Sep 2022 19:37
URI: http://repository.essex.ac.uk/id/eprint/26578

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