Improving Coronavirus (COVID-19) Diagnosis Using Deep Transfer Learning

Background: Coronavirus disease (COVID-19) is an infectious dis- ease caused by a new virus. Exponential growth is not only threatening lives, but also impacting businesses and disrupting travel around the world. Aim: The aim of this work is to develop an efficient diagnosis of COVID-19 disease by differentiating it from viral pneumonia, bacterial pneumonia, and healthy cases using deep learning techniques. Method: In this work, we have used pre-trained knowledge to improve the diagnostic performance using transfer learning techniques and compared the performance of different CNN architectures. Results: Evaluation results using K-fold (10) showed that we have achieved state-of-the-art performance with overall accuracy of 98.75% on the perspective of CT and X-ray cases as a whole. Conclusion: Quantitative evaluation showed high accuracy for automatic diagnosis of COVID-19. Pre-trained deep learning models developed in this study could be used for early screening of coronavirus;however, it calls for extensive need to CT or X-rays dataset to develop a reliable application. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Hot-Spot Zone Detection to Tackle Covid19 Spread by Fusing the Traditional Machine Learning and Deep Learning Approaches of Computer Vision

Corona Virus is a pandemic, and the whole world is affected due to it. Apart from the vaccine, the only cure for this drastic disease is to follow the rules and regulations that avoid further spread. There are different mechanisms like (Social Distancing, Mask Detection, Human occupancy etc.) through which we can able to stop the spread of the coronavirus. In this paper, we proposed hotspot zone detection using the computer vision techniques of deep learning. We have defined the hotspot area as the particular region on which the person touches more than some specified threshold. We further mark that area to some specific color to help the authority take necessary action and disinfect that particular place. To implement this algorithm, we have utilized the human-object interaction concept. We have extracted the dataset of person classes from the publicly available dataset for the person detection and the self-generated dataset to train the algorithm. Different experiments on object detection algorithms (YOLO-v3, Faster RCNN, SSD) for person detection have been performed in this work. We achieved the maximum accuracy in real-time on the YOLO-v3 for person detection. Whereas we have marked the specific area using the template matching algorithm of computer vision techniques. Our proposed algorithm detects the persons and extracts the region of interest points on which the user draws the rectangle. Then we find the intersection over union ratio between the detected person and the region of interest of the marked area to make the decision. We have achieved 88.72% accuracy on person detection in the local environment. Whereas, for the whole system of human-object interaction for detecting the hotspot area zone, we have achieved 86.7% accuracy using the confusion matrix.