An Effective Sparse Autoencoders based Deep Learning Framework for fMRI Scans Classification

Abeer Mahmoud, Hanen Karamti, Fadwa Alrowais

2020

Abstract

Deep Learning (DL) identifies features of medical scans automatically in a way very near to expert doctors and sometimes over beats in treatment procedures. In fact, it increases model generalization as it doesn’t focus on low level features and reduces difficulties (eg: overfitting) of training high dimensional data. Therefore, DL becomes a prioritized choice in building most recent Computer-Aided Diagnosis (CAD) systems. From other prospective, Autism Spectrum Disorder (ASD) is a brain disorder characterized by social miscommunication and confusing repetitive behaviours. The accurate diagnosis of ASD through analysing brain scans of patients is considered a research challenge. Some appreciated efforts has been reported in literature, however the problem still needs enhancement and examination of different models. A multi-phase learning algorithm combining supervised and unsupervised approaches is proposed in this paper to classify brain scans of individuals as ASD or controlled patients (TC). First, unsupervised learning is adopted using two sparse autoencoders for feature extraction and refinement of optimal network weights using back-propagation error minimization. Then, third autoencoder act as a supervised classifier. The Autism Brain fMRI (ABIDE-I) dataset is used for evaluation and cross-validation is performed. The proposed model recorded effective and promising results compared to literatures.

Paper Citation

in Harvard Style

Mahmoud A., Karamti H. and Alrowais F. (2020). An Effective Sparse Autoencoders based Deep Learning Framework for fMRI Scans Classification.In Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-423-7, pages 540-547. DOI: 10.5220/0009397605400547

in Bibtex Style

@conference{iceis20,
author={Abeer Mahmoud and Hanen Karamti and Fadwa Alrowais},
title={An Effective Sparse Autoencoders based Deep Learning Framework for fMRI Scans Classification},
booktitle={Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2020},
pages={540-547},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009397605400547},
isbn={978-989-758-423-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - An Effective Sparse Autoencoders based Deep Learning Framework for fMRI Scans Classification
SN - 978-989-758-423-7
AU - Mahmoud A.
AU - Karamti H.
AU - Alrowais F.
PY - 2020
SP - 540
EP - 547
DO - 10.5220/0009397605400547