Abstract

The number of verified COVID-19 virus infections has continued to climb since the beginning was detected in Wuhan, China, near the year’s conclusion in 2019. The COVID-19 virus can be stopped from spreading if people who are afflicted are recognised immediately and appropriately. CNNs (Convolutional Neural Networks) are a type of Deep-learning, method used to detect visual data anomalies. To increase the CNN technique’s accuracy for recognising COVID-19 patients’ X-ray pictures of the chest, the researchers recommend utilising a transfer learning model. The model of Transfer Learning was employed in this investigation. was DenseNet121. The precision acquired in this study is analysed using the model of transfer learning, which is then compared to standard learning accuracy findings. With regards to the, We employed 1500 chest X-ray images from a large open-source dataset in the typical teaching approach. The settings for epoch and batch size selection, as well as transfer learning, are utilised to evaluate system performance. When using transfer learning methods, pairing parameters with epochs of 100 and batch size 64 give a 98 percent rate of precision, indicating that this is the ideal pair parameter. In terms of classification accuracy, our transfer learning approaches surpass the techniques of traditional education.

Keywords: COVID-19; Convolutional neural networks; Transfer learning; DenseNet121; Accuracy