Automated Detection and Classification of COVID-19 Based on CT Images Using Deep Learning Model

Vidyun, A. S.; Srinivasa Rao, B.; Harikiran, J.

Medical image classification is one of the important areas of application of deep learning. In CT scan images, the structures overlapping against each other are eliminated, thus providing us quality information which helps in classification of images accurately. Diagnosing COVID-19 is the need of the hour and its manual testing consumes a lot of time. Deep learning approach toward COVID CT image classification can reduce this time and provide us with faster results compared to conventional methods. This paper proposes a fine-tuning model, containing a dropout, dense layers, and pretrained model which is validated on publicly built COVID-19 CT scan images, containing 544 COVID and NON-COVID images. The obtained result is compared with different other models like VGG16 and approaches like transfer learning. The experimental result provides us with fine-tuning of the VGG-19 model, which performed better than other models with an overall accuracy of 90.35 ± 0.91, COVID-19 classification accuracy or recall of 92.55 ± 1.25, overall f1-score of 88.75 ± 1.5, and an overall precision of 88.75 ± 1.5. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.