DON: Deep Learning and Optimization-Based Framework for Detection of Novel Coronavirus Disease Using X-ray Images

• Published in: Interdisciplinary sciences : computational life sciences Vol. 13; no. 2; pp. 260 - 272
• Main Authors: Dhiman, Gaurav, Vinoth Kumar, V., Kaur, Amandeep, Sharma, Ashutosh
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.06.2021; Springer Nature B.V
• Subjects: Biomedical and Life Sciences; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Appl. in Life Sciences; Coronaviruses; COVID-19; COVID-19 - diagnostic imaging; COVID-19 - virology; COVID-19 diagnostic tests; Decision Trees; Deep Learning; Diagnosis, Computer-Assisted; Disease transmission; Health Sciences; Humans; Image classification; Life Sciences; Lung - diagnostic imaging; Lung - virology; Mathematical and Computational Physics; Medical imaging; Medicine; Multiple objective analysis; Optimization; Original; Original Research Article; Predictive Value of Tests; Radiographic Image Interpretation, Computer-Assisted; Radiography, Thoracic; Reproducibility of Results; Statistics for Life Sciences; Theoretical; Theoretical and Computational Chemistry; Viral diseases; X-rays; COVID-19; MOEPO; Deep learning; CNN; Coronavirus; Optimization; J48
• ISSN: 1913-2751; 1867-1462; 1867-1462
• DOI: 10.1007/s12539-021-00418-7

In the hospital, a limited number of COVID-19 test kits are available due to the spike in cases every day. For this reason, a rapid alternative diagnostic option should be introduced as an automated detection method to prevent COVID-19 spreading among individuals. This article proposes multi-objective optimization and a deep-learning methodology for the detection of infected coronavirus patients with X-rays. J48 decision tree method classifies the deep characteristics of affected X-ray corona images to detect the contaminated patients effectively. Eleven different convolutional neuronal network-based (CNN) models were developed in this study to detect infected patients with coronavirus pneumonia using X-ray images (AlexNet, VGG16, VGG19, GoogleNet, ResNet18, ResNet500, ResNet101, InceptionV3, InceptionResNetV2, DenseNet201 and XceptionNet). In addition, the parameters of the CNN profound learning model are described using an emperor penguin optimizer with several objectives (MOEPO). A broad review reveals that the proposed model can categorise the X-ray images at the correct rates of precision, accuracy, recall, specificity and F1-score. Extensive test results show that the proposed model outperforms competitive models with well-known efficiency metrics. The proposed model is, therefore, useful for the real-time classification of X-ray chest images of COVID-19 disease.