Fast COVID-19 Detection of Chest X-Ray Images Using Single Shot Detection MobileNet Convolutional Neural Networks

Arifin, Fatchul and Artanto, Herjuna and Nurhasanah, Nurhasanah and Gunawan, Teddy Surya (2021) Fast COVID-19 Detection of Chest X-Ray Images Using Single Shot Detection MobileNet Convolutional Neural Networks. Journal of Southwest Jiaotong University, 56 (2). pp. 235-248. ISSN 0258-2724

PDF Restricted to Repository staff only Download (848kB) | Request a copy

Abstract

COVID-19 is a new disease with a very rapid and tremendous spread. The most important thing needed now is a COVID-19 early detection system that is fast, easy to use, portable, and affordable. Various studies on desktop-based detection using Convolutional Neural Networks have been successfully conducted. However, no research has yet applied mobile-based detection, which requires low computational cost. Therefore, this research aims to produce a COVID-19 early detection system based on chest X-ray images using Convolutional Neural Network models to be deployed in mobile applications. It is expected that the proposed Convolutional Neural Network models can detect COVID-19 quickly, economically, and accurately. The used architecture is MobileNet's Single Shot Detection. The advantage of the Single Shot Detection MobileNet models is that they are lightweight to be applied to mobile-based devices. Therefore, these two versions will also be tested, which one is better. Both models have successfully detected COVID-19, normal, and viral pneumonia conditions with an average overall accuracy of 93.24% based on the test results. The Single Shot Detection MobileNet V1 model can detect COVID-19 with an average accuracy of 83.7%, while the Single Shot Detection MobileNet V2 Single Shot Detection model can detect COVID-19 with an average accuracy of 87.5%. Based on the research conducted, it can be concluded that the approach to detecting chest X-rays of COVID-19 can be detected using the MobileNet Single Shot Detection model. Besides, the V2 model shows better performance than the V1. Therefore, this model can be applied to increase the speed and affordability of COVID-19 detection.

Item Type:	Article (Journal)
Uncontrolled Keywords:	Convolutional Neural Network, Covid-19, Chest X-Ray Image, Deep Learning, MobileNet
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	Prof. Dr. Teddy Surya Gunawan
Date Deposited:	18 May 2021 11:46
Last Modified:	18 May 2021 11:46
URI:	http://irep.iium.edu.my/id/eprint/89879

Actions (login required)

View Item