COVID-19 Semantic Pneumonia Segmentation and Classification Using Artificial Intelligence.

Autor:	Abdulaal MJ; Department of Electrical and Computer Engineering (ECE), King Abdulaziz University, Jeddah, Saudi Arabia.; Center of Excellence in Intelligent Engineering Systems (CEIES), King Abdulaziz University, Jeddah, Saudi Arabia., Mehedi IM; Department of Electrical and Computer Engineering (ECE), King Abdulaziz University, Jeddah, Saudi Arabia.; Center of Excellence in Intelligent Engineering Systems (CEIES), King Abdulaziz University, Jeddah, Saudi Arabia., Abusorrah AM; Department of Electrical and Computer Engineering (ECE), King Abdulaziz University, Jeddah, Saudi Arabia., Aljohani AJ; Department of Electrical and Computer Engineering (ECE), King Abdulaziz University, Jeddah, Saudi Arabia.; Center of Excellence in Intelligent Engineering Systems (CEIES), King Abdulaziz University, Jeddah, Saudi Arabia., Milyani AH; Department of Electrical and Computer Engineering (ECE), King Abdulaziz University, Jeddah, Saudi Arabia., Rana MM; Department of Electrical and Electronic Engineering, Rajshahi University of Engineering and Technology (RUET), Rajshahi, Bangladesh., Mahmoud M; Electrical and Computer Engineering Department, Tennessee Technological University, Cookeville, TN, USA.
Jazyk:	angličtina
Zdroj:	Contrast media & molecular imaging [Contrast Media Mol Imaging] 2022 Sep 15; Vol. 2022, pp. 5297709. Date of Electronic Publication: 2022 Sep 15 (Print Publication: 2022).
DOI:	10.1155/2022/5297709
Abstrakt:	Coronavirus 2019 (COVID-19) has become a pandemic. The seriousness of COVID-19 can be realized from the number of victims worldwide and large number of deaths. This paper presents an efficient deep semantic segmentation network (DeepLabv3Plus). Initially, the dynamic adaptive histogram equalization is utilized to enhance the images. Data augmentation techniques are then used to augment the enhanced images. The second stage builds a custom convolutional neural network model using several pretrained ImageNet models and compares them to repeatedly trim the best-performing models to reduce complexity and improve memory efficiency. Several experiments were done using different techniques and parameters. Furthermore, the proposed model achieved an average accuracy of 99.6% and an area under the curve of 0.996 in the COVID-19 detection. This paper will discuss how to train a customized smart convolutional neural network using various parameters on a set of chest X-rays with an accuracy of 99.6%. Competing Interests: The authors declare that they have no conflicts of interest. (Copyright © 2022 Mohammed J. Abdulaal et al.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu