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\(\mathrm SRC_{2}\): a novel deep learning based technique for identifying COVID-19 using images of chest x-ray

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Abstract

COVID-19 has affected more than 520 million population worldwide by April 2022. Few medical examinations such as rapid antigen and RT-PCR are recommended for timely diagnosis of COVID-19. However, RT-PCR and other similar type of tests can show the presence of virus only within eight to thirteen days. Furthermore, the chest related complications that arise as a result of COVID-19 (such as pneumonia and Acute Respiratory Distress Syndrome (ARDS)) cannot be diagnosed through these tests. In the present article, we have proposed a deep Convolutional Neural Network (CNN) based model, named \(\mathrm SRC_{2}\) that can differentiate between a normal and COVID-19 affected Chest X-Ray image. The proposed model consists of two modules, \(\mathrm SRC_{2}\)F and \(\mathrm SRC_{2}\)C for feature extraction and classification respectively. We have done feature extraction using existing trained CNN models and our proposed model \(\mathrm SRC_{2}\)F. Then, we have implemented standard classifiers and our proposed classifier \(\mathrm SRC_{2}\)C over the extracted features. Finally, we have compared the performance of our proposed Feature extractor module and classification module. Experimental results show that our proposed model \(\mathrm SRC_{2}\) has obtained an accuracy of 98.68 percent which is better than the existing methods.

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Data Availability

Data sharing is not applicable to this article as no new datasets were generated or analyzed during the current study. The datasets used in this article are from freely available medical images can be downloaded online from the links [11, 12].

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  1. Motilal Nehru National Institute of Technology Allahabad, Prayagraj, India

    Utsav Acharya, Shashwati Banerjea & Rajitha B

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  1. Utsav Acharya
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  2. Shashwati Banerjea
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  3. Rajitha B
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Acharya, U., Banerjea, S. & B, R. \(\mathrm SRC_{2}\): a novel deep learning based technique for identifying COVID-19 using images of chest x-ray. Multimed Tools Appl 83, 40773–40790 (2024). https://doi.org/10.1007/s11042-023-16983-6

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