Abstract
This research work aims to identify COVID-19 through deep learning models using lung CT-SCAN images. In order to enhance lung CT scan efficiency, a super-residual dense neural network was applied. The experimentation has been carried out using benchmark datasets like SARS-COV-2 CT-Scan and Covid-CT Scan. To mark COVID-19 as positive or negative for the improved CT scan, existing pre-trained models such as XceptionNet, MobileNet, InceptionV3, DenseNet, ResNet50, and VGG (Visual Geometry Group)16 have been used. Taking CT scans with super resolution using a residual dense neural network in the pre-processing step resulted in improving the accuracy, F1 score, precision, and recall of the proposed model. On the dataset Covid-CT Scan and SARS-COV-2 CT-Scan, the MobileNet model provided a precision of 94.12% and 100% respectively.
【저자키워드】 COVID-19, pandemic, deep learning, CT scan, transfer learning, Mobilenet, 【초록키워드】 lung, Accuracy, Research, dataset, recall, Efficiency, Visual, lung CT scan, Precision, positive, ENhance, identify, carried, provided, applied, Taking, 【제목키워드】 lung CT scan, approach,