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Deep Learning ResNet101 Deep Features of Portable Chest X-Ray Accurately Classify COVID-19 Lung Infection

Sobia Nawaz1, Sidra Rasheed2, Wania Sami3, Lal Hussain4,5,*, Amjad Aldweesh6,*, Elsayed Tag eldin7, Umair Ahmad Salaria8,9, Mohammad Shahbaz Khan10

1 Basic Health Unit Sumra, Lodhran, 59320, Pakistan
2 Basic Health Unit 20-G, Chishtian, Bahawalnagar, 62300, Pakistan
3 Doctor’s Hospital, Lahore, 54590, Pakistan
4 Department of Computer Science & IT, Neelum Campus, The University of Azad Jammu and Kashmir, Athmuqam, 13230, Azad Kashmir, Pakistan
5 Department of Computer Science & IT, King Abdullah Campus, The University of Azad Jammu and Kashmir, Muzaffarabad, 13100, Azad Kashmir, Pakistan
6 College of Computer science and information technology, Shaqra University, Shaqra, 15273, Saudi Arabia
7 Faculty of Engineering and Technology, Future University in Egypt, New Cairo, 11835, Egypt
8 Department of Electrical Engineering, University of Azad Jammu and Kashmir, Chehla Campus, Muzaffarabad, 13100, Azad Kashmir, Pakistan
9 Department of Electrical Engineering, Mirpur University of Science & Technology, Mirpur, Muzaffarabad, 10250, Azad Kashmir, Pakistan
10 Children’s National Hospital, 111 Michigan AVE NW, Washington, DC, 20854, USA

* Corresponding Authors: Lal Hussain. Email: email; Amjad Aldweesh. Email: email

Computers, Materials & Continua 2023, 75(3), 5213-5228. https://doi.org/10.32604/cmc.2023.037543

Abstract

This study is designed to develop Artificial Intelligence (AI) based analysis tool that could accurately detect COVID-19 lung infections based on portable chest x-rays (CXRs). The frontline physicians and radiologists suffer from grand challenges for COVID-19 pandemic due to the suboptimal image quality and the large volume of CXRs. In this study, AI-based analysis tools were developed that can precisely classify COVID-19 lung infection. Publicly available datasets of COVID-19 (N = 1525), non-COVID-19 normal (N = 1525), viral pneumonia (N = 1342) and bacterial pneumonia (N = 2521) from the Italian Society of Medical and Interventional Radiology (SIRM), Radiopaedia, The Cancer Imaging Archive (TCIA) and Kaggle repositories were taken. A multi-approach utilizing deep learning ResNet101 with and without hyperparameters optimization was employed. Additionally, the features extracted from the average pooling layer of ResNet101 were used as input to machine learning (ML) algorithms, which twice trained the learning algorithms. The ResNet101 with optimized parameters yielded improved performance to default parameters. The extracted features from ResNet101 are fed to the k-nearest neighbor (KNN) and support vector machine (SVM) yielded the highest 3-class classification performance of 99.86% and 99.46%, respectively. The results indicate that the proposed approach can be better utilized for improving the accuracy and diagnostic efficiency of CXRs. The proposed deep learning model has the potential to improve further the efficiency of the healthcare systems for proper diagnosis and prognosis of COVID-19 lung infection.

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Cite This Article

APA Style
Nawaz, S., Rasheed, S., Sami, W., Hussain, L., Aldweesh, A. et al. (2023). Deep learning resnet101 deep features of portable chest x-ray accurately classify COVID-19 lung infection. Computers, Materials & Continua, 75(3), 5213-5228. https://doi.org/10.32604/cmc.2023.037543
Vancouver Style
Nawaz S, Rasheed S, Sami W, Hussain L, Aldweesh A, eldin ET, et al. Deep learning resnet101 deep features of portable chest x-ray accurately classify COVID-19 lung infection. Comput Mater Contin. 2023;75(3):5213-5228 https://doi.org/10.32604/cmc.2023.037543
IEEE Style
S. Nawaz et al., "Deep Learning ResNet101 Deep Features of Portable Chest X-Ray Accurately Classify COVID-19 Lung Infection," Comput. Mater. Contin., vol. 75, no. 3, pp. 5213-5228. 2023. https://doi.org/10.32604/cmc.2023.037543



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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