Title: Contactless non-invasive method to identify abnormal tongue area using K-mean and problem identification in COVID-19 scenario

Authors: Pallavi Pahadiya; Ritu Vijay; Kumod Kumar Gupta; Shivani Saxena; Ritu Tandon

Addresses: Banasthali Vidyapith, Rajasthan, India; SAGE University, Indore, India ' Electronics Department, Banasthali Vidyapith, Rajasthan, India ' Banasthali Vidyapith, Rajasthan, India; Delhi Technical Campus, India ' Banasthali Vidyapith, Rajasthan, India ' Computer Science and Engineering Department, SAGE University, Indore, India

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.

Keywords: tongue diagnosis system; TDS; image acquisition; thresholding; segmentation; K-mean clustering; mobile app.

DOI: 10.1504/IJMEI.2022.125310

International Journal of Medical Engineering and Informatics, 2022 Vol.14 No.5, pp.379 - 390

Received: 16 May 2020
Accepted: 20 Oct 2020
Published online: 07 Sep 2022 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article