Journal of Medical Physics
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Year : 2021  |  Volume : 46  |  Issue : 3  |  Page : 189-196

Deep learning approach for analyzing the COVID-19 chest x-rays

1 Department of Radiotherapy, S. N. Medical College, Agra, Uttar Pradesh, India
2 Department of Physics, GLA University, Mathura, Uttar Pradesh, India
3 Department of Radiodiagnosis, S. N. Medical College, Agra, Uttar Pradesh, India

Correspondence Address:
Dr. Anuj Kumar
Department of Radiotherapy, S. N. Medical College, Agra-282002, Uttar Pradesh
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jmp.JMP_22_21

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Purpose The purpose of this study is to analyze the utility of Convolutional Neural Network (CNN) in medical image analysis. In this study, deep learning (DL) models were used to classify the X-ray into COVID, viral pneumonia, and normal categories. Materials and Methods In this study, we have compared the results 9 layers CNN model (9 LC) developed by us with 2 transfer learning models (Visual Geometry Group) 16 and VGG19. Two different datasets used in this study were obtained from the Kaggle database and the Radiodiagnosis department of our institution. Results In our study, VGG16 yields the highest accuracy among all three models for different datasets as the Kaggle dataset-94.96% and the department of Radiodiagnosis dataset 85.71%. Although, the precision was found better while using 9 LC and VGG19 for both datasets. Conclusions DL can help the radiologists in the speedy prediction of diseases and detecting minor features of the disease which may be missed by the human eye. In the present study, we have used three models, i.e.,, CNN with 9 LCs, VGG16, and VGG19 transfer learning models for the classification of X-ray images with good accuracy and precision. DL may play a key role in analyzing the medical image dataset.

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