The covid-19 detection with contactless method based on deep learning

Ismail, undefined and Fibriyanti, , and Aidha, Zass Ressy and Menhendry, , and Kamarul Hawari, Ghazali (2020) The covid-19 detection with contactless method based on deep learning. In: 3rd International Conference on Applied Science and Technology, iCAST 2020. 3rd International Conference on Applied Science and Technology, iCAST 2020 , 24 - 25 October 2020 , Padang. pp. 241-245.. ISBN 978-172819567-4 (Published)

	Pdf The Covid-19 detection with contactless method based on deep learning.pdf Restricted to Repository staff only Download (1MB) | Request a copy
Preview	Pdf The Covid-19 detection with contactless method based on deep learning_ABS.pdf Download (656kB) | Preview

Abstract

The spread of corona virus diseases among medical workers is a big problem in this pandemic corona virus. The are many medical workers suffer and some of them died. The infected medical workers of corona virus are caused through directed or closed contacts between infected patients and medical workers. The closed and directed contact in medical services take place especially in diagnostic process. In order to handle this problem, the proposed method prevents the closed or directed contacts of medical workers to the suspected patients. The method uses RMI images or chest X-ray images data to predict the infected suspects. The proposed method is detecting the infected lung X-Ray image through Deep Learning model. It uses pre-trained model of GoogleNet, with modification. The Confusion Matrix and ROC curve are used to measure accuracy the predicted. They show that proposed method has high accuracy . Finally, the proposed method is able to replace closed or directed contact diagnostic done by medical workers to Covid-19 suspects. It has ability to handle the spread of Covid-19 among medical workers.

Item Type:	Conference or Workshop Item (Lecture)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Chest X-ray; Contactless method; Corona virus; Medical workers
Subjects:	T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division:	Faculty of Electrical and Electronic Engineering Technology
Depositing User:	Mr Muhamad Firdaus Janih@Jaini
Date Deposited:	02 Dec 2024 01:13
Last Modified:	02 Dec 2024 01:13
URI:	http://umpir.ump.edu.my/id/eprint/42457
Download Statistic:	View Download Statistics

Actions (login required)

View Item