COV19X-Net: A Convolutional Neural Network-based method for classifying COVID-19 in chest X-ray images
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2023 26th International Conference on Computer and Information Technology (ICCIT), 2024, 00, pp. 1-6
- Issue Date:
- 2024-02-27
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| COV19X-Net_A_Convolutional_Neural_Network-based_method_for_classifying_COVID-19_in_chest_X-ray_images.pdf | Published version | 1.39 MB |
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The SARS CoV 2 virus is the infectious pathogen responsible for COVID 19 Early COVID 19 diagnosis is crucial for the proper treatment of the infected patient Popular detection method like RT PCR can significantly reduce the effectiveness of diagnosis and it is costly and insensitive to diagnose COVID 19 That is why an automated approach for the detection of COVID 19 from medical images is essential to make accurate fast and cost effective prediction This study introduces the application of a proposed model named COV19X Net based on InceptionResNetV2 a pre trained Convolutional Neural Network architecture COV19X Net is trained and tested on three chest X ray datasets COVID 19 is one of the four classes of chest radiography pictures in these datasets To our knowledge there have been a very few comparisons of automated approaches for 4 class COVID 19 classification on diverse datasets As a result this study has evaluated the efficacy of the proposed model COV19X Net to that of previously well known research works using a variety of metrics including accuracy precision recall rate and F1 score With accuracy of 86 36 94 42 and 91 88 respectively on three datasets the suggested model COV19X Net has performed better on test data that has not been pre processed
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