Paper Info
Title
Deep Neural Network-Based Screening Model For Covid-19-Infected Patients Using Chest X-Ray Images
Abstract
There are limited coronavirus disease 2019 (COVID-19) testing kits, therefore, development of other diagnosis approaches is desirable. The doctors generally utilize chest X-rays and Computed Tomography (CT) scans to diagnose pneumonia, lung inflammation, abscesses, and/or enlarged lymph nodes. Since COVID-19 attacks the epithelial cells that line our respiratory tract, therefore, X-ray images are utilized in this paper, to classify the patients with infected (COVID-19 +ve) and uninfected (COVID-19 -ve) lungs. Almost all hospitals have X-ray imaging machines, therefore, the chest X-ray images can be used to test for COVID-19 without utilizing any kind of dedicated test kits. However, the chest X-ray-based COVID-19 classification requires a radiology expert and significant time, which is precious when COVID-19 infection is increasing at a rapid rate. Therefore, the development of an automated analysis approach is desirable to save the medical professionals' valuable time. In this paper, a deep convolutional neural network (CNN) approach is designed and implemented. Besides, the hyper-parameters of CNN are tuned using Multi-objective Adaptive Differential Evolution (MADE). Extensive experiments are performed by considering the benchmark COVID-19 dataset. Comparative analysis reveals that the proposed technique outperforms the competitive machine learning models in terms of various performance metrics.
Year
DOI
Venue
2021
10.1142/S0218001421510046
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
Keywords
DocType
Volume
Chest X-ray, COVID-19, classification, deep neural networks
Journal
35
Issue
ISSN
Citations 
3
0218-0014
2
PageRank 
References 
Authors
0.40
0
4
Name
Order
Citations
PageRank
Dilbag Singh16715.16
Vijay Kumar222921.59
Vaishali Yadav320.40
Manjit Kaur4238.41