RESUMEN
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.
RESUMEN
Various clinical studies and researchers have established that chest CT scans provide an accurate clinical diagnosis on the detection of COVID-19. The traditional gold standard RT-PCR testing methodology might give false positive and false negative results than the desired rates. AI has proven to be the driving force in developing various COVID-19 management tools. Provided with the situation of lack of datasets, we applied a transfer learning approach to detect COVID-19 from chest CT images. The previous work observed that the VGG-19 has better performance with medical image data compared to other deep learning models such as VGG-16, InceptionV3, DenseNet121, which showed overfitting in the initial epochs. This study determined the best performing parameters for the VGG-19 transfer learning model to classify COVID-19 cases and healthy cases. We experimented with the model against three parameters: activation function, loss function, and training batch size. After the analysis, we found that the VGG-19 model with SoftMax activation function, Categorical cross-entropy loss function, and training batch size as 32 has the highest accuracy of 93%. © 2022 Bharati Vidyapeeth, New Delhi.