Performance Analysis of VGG-16 Deep Learning Model for COVID-19 Detection using Chest X-Ray Images
Proceedings of the 17th INDIACom|2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023
; : 1001-1007, 2023.
Artículo
en Inglés
| Scopus | ID: covidwho-20235248
ABSTRACT
COVID-19 is an infectious disease caused by newly discovered coronavirus. Currently, RT-PCR and Rapid Testing are used to test a person against COVID-19. These methods do not produce immediate results. Hence, we propose a solution to detect COVID-19 from chest X-ray images for immediate results. The solution is developed using a convolutional neural network architecture (VGG-16) model to extract features by transfer learning and a classification model to classify an input chest X-ray image as COVID-19 positive or negative. We introduced various parameters and computed the impact on the performance of the model to identify the parameters with high impact on the model's performance. The proposed solution is observed to provide best results compared to the existing ones. © 2023 Bharati Vidyapeeth, New Delhi.
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Bases de datos de organismos internacionales
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Inglés
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Proceedings of the 17th INDIACom|2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023
Año:
2023
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Artículo
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