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Rethinking the Transfer Learning Architecture for Respiratory Diseases and COVID-19 Diagnosis

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Biomedical Data Analysis and Processing Using Explainable (XAI) and Responsive Artificial Intelligence (RAI)

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 222))

Abstract

The emergence of COVID-19 has caused a disastrous scenario worldwide, becoming one of the most acute and deadly diseases in the last century wreaking havoc on the health and lives of countless people. The prevalence rate of COVID-19 is growing significantly every day across the world. One critical step in combating COVID-19 is the capacity to identify infected individuals and place them in special care as soon as possible. Detecting this condition via radiography and radiology images is one of the quickest ways to diagnose patients. Early study has found specific abnormalities in the chest radiographs of infected individuals with COVID-19. Inspired by prior research, we examine the application of transfer learning models to detect COVID-19 patients in X-rays. In this study, an X-ray image collection from patients with common bacterial pneumonia, viral pneumonia, proven COVİD-19 disease, and normal occurrences was used to diagnose coronavirus disease automatically. A dataset has been used in this experiment comprising 76 image samples showing verified COVID-19 illness, 2786 images showing bacterial pneumonia, 1504 images showing viral pneumonia, and 1583 images showing normal circumstances. The information was gathered from publicly accessible X-ray images. Data augmentation technique is applied to the trained image dataset. Two transfer learning models, namely, VGG 16 and Xception, have been modified in this paper after applying additional layers with the base model. Modified Xception model provides an overall accuracy of 84.82% for Adam optimizer and 78.40% for RMSprop optimizer. Modified VGG 16 model provides an overall accuracy of 84.98% for Adam optimizer and 83.88% for RMSprop optimizer. In addition to accuracy, we show each model’s receiver operating characteristic (ROC) curve, precision, recall, F1-score, and AUC.

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Author information

Authors and Affiliations

  1. Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, 1205, Bangladesh

    Prajoy Podder, Subrato Bharati & M. Rubaiyat Hossain Mondal

  2. Department of Computer Science, Babasaheb Bhimrao Ambedkar University (Central University), Satellite Centre, Amethi, UP, India

    Aditya Khamparia

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  1. Prajoy Podder
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  2. Subrato Bharati
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  3. M. Rubaiyat Hossain Mondal
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  4. Aditya Khamparia
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Corresponding author

Correspondence to Subrato Bharati .

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Editors and Affiliations

  1. Department of Computer Science, Satellite Centre, Babasaheb Bhimrao Ambedkar University, Amethi, Uttar Pradesh, India

    Aditya Khamparia

  2. Department of Computer Science Engineering, Maharaja Agrasen Institute of Technology, Rohini, Delhi, India

    Deepak Gupta

  3. Maharaja Agrasen Institute of Technology, Rohini, India

    Ashish Khanna

  4. Department of Automatics and Applied Software, Faculty of Engineering, Aurel Vlaicu University of Arad, Arad, Arad, Romania

    Valentina E. Balas

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Podder, P., Bharati, S., Mondal, M.R.H., Khamparia, A. (2022). Rethinking the Transfer Learning Architecture for Respiratory Diseases and COVID-19 Diagnosis. In: Khamparia, A., Gupta, D., Khanna, A., Balas, V.E. (eds) Biomedical Data Analysis and Processing Using Explainable (XAI) and Responsive Artificial Intelligence (RAI). Intelligent Systems Reference Library, vol 222. Springer, Singapore. https://doi.org/10.1007/978-981-19-1476-8_8

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