Research Repository

Generalization Performance of Deep Learning Models in Neurodegenerative Disease Classification

Yagis, E and De Herrera, AGS and Citi, L (2019) Generalization Performance of Deep Learning Models in Neurodegenerative Disease Classification. In: UNSPECIFIED, ? - ?.

[img]
Preview
Text
IEEE_BIBM_2019.pdf - Accepted Version

Download (644kB) | Preview

Abstract

© 2019 IEEE. 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 state-of-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
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 27 Jan 2020 11:08
Last Modified: 26 May 2020 20:15
URI: http://repository.essex.ac.uk/id/eprint/26578

Actions (login required)

View Item View Item