ABSTRACT
Due to the spread of COVID-19 all around the world, there is a need of automatic system for primary tongue ulcer cancerous cell detection since everyone do not go to hospital due to the panic and fear of virus spread. These diseases if avoided may spread soon. So, in such a situation, there is global need of improvement in disease sensing through remote devices using non-invasive methods. Automatic tongue analysis supports the examiner to identify the problem which can be finally verified using invasive methods. In automated tongue analysis image quality, segmentation of the affected region plays an important role for disease identification. This paper proposes mobile-based image sensing and sending the image to the examiner, if examiner finds an issue in the image, the examiner may guide the user to go for further treatment. For segmentation of abnormal area, K-mean clustering is used by varying its parameters.Copyright © 2022 Inderscience Enterprises Ltd.
ABSTRACT
According to the WHO, COVID 19 virus is spread by respiratory droplets and personal contact. To prevent the transmission of this virus, the use of masks and social isolation is recommended. Because COVID 19 virus droplets may fall on any surface, the more crucial of the two measures is to wear a mask. It is vital to keep track of who is and is not wearing a mask. To comply with regulatory requirements, a mask recognition system capable of recognizing any kind of mask as well as masks in a variety of configurations inside video streams has been developed. To detect masks from images/video streams, a deep learning approach and the Python TensorFlow, Keras, and Pytorch packages are utilized. The suggested technology is capable of distinguishing persons who use masks from those who do not. © 2022 IEEE.