Fast Automated Detection of COVID-19 from CT Images Using Transfer Learning Approach

ABSTRACT

A long clinical testing period is one of the key elements for the COVID-19 pandemic's fast spread. Controlling the spread of COVID-19 requires early detection and diagnosis. Chest X-ray (CXR), for example, is an imaging technology that helps to speed up the identifying procedure of COVID-19 in patients. As a result, our goal is to create an automatic CAD system that can recognize COVID-19 samples from healthy people and COVID patients using CT scans. We used transfer learning (TL) approach, i.e., modified Visual Geometry Group (VGG19) and compared our proposed system results with other machine learning (ML) and deep learning (DL) approaches in order to discover the best one for this job. The proposed technique and various DL and ML models are tested using the COVID-CT dataset, where 80% of images are utilized for training and 20% for testing purpose. Our proposed TL technique achieves 97.83% classification accuracy with average precision, recall, and F1-score of 98.33, 97.67, and 97.67, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.