loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Abeer M. Mahmoud 1 ; Hanen Karamti 2 and Fadwa Alrowais 3

Affiliations: 1 Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt, Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, PO Box 84428, Riyadh, Saudi Arabia ; 2 Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, PO Box 84428, Riyadh, Saudi Arabia, MIRACL Laboratory, ISIMS, University of Sfax, B.P. 242, 3021 Sakiet Ezzit, Sfax, Tunisia ; 3 Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, PO Box 84428, Riyadh, Saudi Arabia

Keyword(s): Sparse Autoencoder, Autism, Medical Image Classification.

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 pat ients (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. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.233.242.67

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
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; ISSN 2184-4992, SciTePress, pages 540-547. DOI: 10.5220/0009397605400547

@conference{iceis20,
author={Abeer M. 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},
issn={2184-4992},
}

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
IS - 2184-4992
AU - Mahmoud, A.
AU - Karamti, H.
AU - Alrowais, F.
PY - 2020
SP - 540
EP - 547
DO - 10.5220/0009397605400547
PB - SciTePress