The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
A Software for Thorax Images Analysis Based on Deep Learning
|
|
Author(s): Ahmed H. Almulihi (Department of Computer Science, College of Computers and Information Technology, Taif University, Saudi Arabia), Fahd S. Alharithi (Department of Computer Science, College of Computers and Information Technology, Taif University, Saudi Arabia), Seifeddine Mechti (Department of Computer Science, Sfax University, Tunisia), Roobaea Alroobaea (Department of Computer Science, College of Computers and Information Technology, Taif University, Saudi Arabia)and Saeed Rubaiee (Department of Industrial and Systems Engineering, University of Jeddah, Jeddah, Saudi Arabia)
Copyright: 2021
Volume: 12
Issue: 1
Pages: 12
Source title:
International Journal of Open Source Software and Processes (IJOSSP)
Editor(s)-in-Chief: Marta Catillo (Università degli Studi del Sannio, Italy)
DOI: 10.4018/IJOSSP.2021010104
Purchase
|
Abstract
People suspected of having COVID-19 need to know quickly if they are infected, so that they can isolate themselves, receive treatment, and inform those with whom they have been in close contact. Currently, the formal diagnosis of COVID-19 infection requires laboratory analysis of blood samples or swabs from the throat and nose. The lab test requires specialized equipment and takes at least 24 hours to produce a result. For this reason, in this paper, the authors tackle the problem of the detection of COVID-19 by developing an open source software to analyze chest x-ray thorax images. The method is based on supervised learning applied to 5000 images. However, deep learning techniques such as convolutional neural network (CNN) and mask R-CNN gives good results comparing with classic medical imaging. Using a dynamic learning rate, they obtained 0.96 accuracy for the training phase and 0.82 for the test. The results of our free tool are comparable to the best state of the art open source systems.
Related Content
|
Roland Robert Schreiber.
© 2023.
20 pages.
|
|
Sushil Kumar, SK Muttoo, V. B. Singh.
© 2022.
16 pages.
|
|
Satya Sobhan Panigrahi, Ajay Kumar Jena.
© 2022.
20 pages.
|
|
Ekbal Rashid, Mohan Prakash.
© 2022.
16 pages.
|
|
Ritu Garg, Rakesh Kumar Singh.
© 2022.
18 pages.
|
|
Neelamadhab Padhy, Sanskruti Panda, Jigyashu Suraj.
© 2022.
20 pages.
|
|
Anil Kumar Patidar, Ugrasen Suman.
© 2022.
17 pages.
|
|
|