Contactless non-invasive method to identify abnormal tongue area using K-mean and problem identification in COVID-19 scenario
International Journal of Medical Engineering and Informatics
; 14(5):379-390, 2022.
Article
in English
| ProQuest Central | ID: covidwho-2022020
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.
Medical Sciences--Computer Applications; tongue diagnosis system; TDS; image acquisition; thresholding; segmentation; K-mean clustering; mobile app; Image processing; Viruses; COVID-19; Image quality; Viral diseases; Tongue; Image segmentation; Identification methods; Clustering; Medical imaging; Remote sensing
Full text:
Available
Collection:
Databases of international organizations
Database:
ProQuest Central
Language:
English
Journal:
International Journal of Medical Engineering and Informatics
Year:
2022
Document Type:
Article
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