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Data Augmentation and Fine-Tuning the Radiography Images to Detect COVID-19 Patients with Pre-trained Network of Transfer Learning

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Proceedings of Data Analytics and Management

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 90))

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Abstract

Around 26 million cases of COVID-19 have been acquired and currently are in inventory and still recorded worldwide as a consequence of the COVID-19 outbreak. For the automated identification of the coronavirus, a radiography imaging dataset of patients with common viral pneumonia, lungs opacity, reported COVID-19 patients, and regular patients was used in this study. The study’s aim is to assess the efficiency of state-of-the-art VGG-16 architectures for medical image, i.e., CXR classification that has been proposed in recent years. Specifically, the technique of transfer learning was implemented. The identification of numerous abnormalities in specific medical image databases is an achievable goal with transfer learning, and the findings are often impressive. There are four groups of the datasets used in this experiment. In the first class, i.e., viral pneumonia, a collection of 1345 imaging images, in second class, i.e., lungs opacity having 6012 images, in third class, i.e., confirmed COVID-19 having 3616 images, and in fourth class, i.e., normal lungs having 10,192 images. The dataset was compiled using publicly accessible X-ray files from medical databases. The results show that transfer learning with X-ray visualization can extract biomarkers that are critical for the COVID-19 disease, whereas the best trainable accuracy and validation accuracy are achieved 97.57% and 93.48%, respectively. Because all diagnostic instruments already have failure rates that are cause for concern, the medical profession should determine the likelihood of integrating X-rays into disease detection based on the results, although further studies to examine the X-ray imaging method from various perspectives should be undertaken.

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Author information

Authors and Affiliations

  1. School of Computer Science Engineering, Lovely Professional University, Phagwara, Punjab, India

    Birjit Gope & Rachna Kohar

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  1. Birjit Gope
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  2. Rachna Kohar
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Editor information

Editors and Affiliations

  1. Maharaja Agrasen Institute of Technology, New Delhi, India

    Deepak Gupta

  2. Jan Wyzykowski University, Polkowice, Poland

    Zdzislaw Polkowski

  3. Maharaja Agrasen Institute of Technology, New Delhi, India

    Ashish Khanna

  4. Rajnagar Mahavidyalaya, Birbhum, West Bengal, India

    Siddhartha Bhattacharyya

  5. Tijuana Institute of Technology, Tijuana, Mexico

    Oscar Castillo

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Gope, B., Kohar, R. (2022). Data Augmentation and Fine-Tuning the Radiography Images to Detect COVID-19 Patients with Pre-trained Network of Transfer Learning. In: Gupta, D., Polkowski, Z., Khanna, A., Bhattacharyya, S., Castillo, O. (eds) Proceedings of Data Analytics and Management . Lecture Notes on Data Engineering and Communications Technologies, vol 90. Springer, Singapore. https://doi.org/10.1007/978-981-16-6289-8_65

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