Deep Learning Approach for Analyzing the COVID-19 Chest X-Rays.
| Autor: | Manav M; Department of Radiotherapy, S. N. Medical College, Agra, Uttar Pradesh, India., Goyal M; Department of Physics, GLA University, Mathura, Uttar Pradesh, India., Kumar A; Department of Radiotherapy, S. N. Medical College, Agra, Uttar Pradesh, India., Arya AK; Department of Radiotherapy, S. N. Medical College, Agra, Uttar Pradesh, India., Singh H; Department of Radiodiagnosis, S. N. Medical College, Agra, Uttar Pradesh, India., Yadav AK; Department of Radiotherapy, S. N. Medical College, Agra, Uttar Pradesh, India. |
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| Jazyk: | angličtina |
| Zdroj: | Journal of medical physics [J Med Phys] 2021 Jul-Sep; Vol. 46 (3), pp. 189-196. Date of Electronic Publication: 2021 Sep 08. |
| DOI: | 10.4103/jmp.JMP_22_21 |
| Abstrakt: | 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. Competing Interests: There are no conflicts of interest. (Copyright: © 2021 Journal of Medical Physics.) |
| Databáze: | MEDLINE |
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