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      Deep Neural Network-Based Screening Model for COVID-19-Infected Patients Using Chest X-Ray Images

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          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 [Formula: see text]ve) and uninfected (COVID-19 [Formula: see text]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.

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          Author and article information

          Journal
          International Journal of Pattern Recognition and Artificial Intelligence
          Int. J. Patt. Recogn. Artif. Intell.
          World Scientific Pub Co Pte Lt
          0218-0014
          1793-6381
          October 10 2020
          : 2151004
          Affiliations
          [1 ]Computer Science Engineering, School of Engineering and Applied Sciences, Bennett University, Greater Noida, 201310, India
          [2 ]National Institute of Technology Hamirpur, Hamirpur 177005, Himachal Pradesh, India
          [3 ]Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur 303007, Rajasthan, India
          Article
          10.1142/S0218001421510046
          © 2020

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