Retracing-efficient IoT model for identifying the skin-related tags using automatic lumen detection

Issue title: Machine Learning Based Computational Bioinformatics for Healthcare Big Data

Guest editors: Muhammad Attique Khan, Gaurav Dhiman and Sathishkumar VE

Article type: Research Article

Authors: Vivekananda, G.N.^{a; *} | Almufti, Saman M.^b | Suresh, C.^c | Samsudeen, Salomi^d | Devarajan, Mohanarangan Veerapperumal^e | Srikanth, R.^f | Jayashree, S.^g

Affiliations: [a] School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, India | [b] Computer Science Department, Nawroz University, Kurdistan Region, Dahuk, Iraq | [c] Department of Computer Science and Engineering, KalaignarKarunanidhi Institute of Technology, Coimbatore, India | [d] Department of Computational Intelligence, SRM Institute of Science & Technology, K.T.R. Campus, Chennai, India | [e] Ernst & Young, New York, NY, USA | [f] Department of Computer Science & Engineering, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India | [g] Department of Computer Science and Engineering, KGISL Institute of Technology, Coimbatore, Tamil Nadu, India

Correspondence: [*] Corresponding author: G.N. Vivekananda, School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, India. E-mail: [email protected].

Abstract: The number of patients with skin diseases reported a dramatic increase which is a major concern and should be addressed. The evaluation of skin is crucial to the correct diagnosis during the follow-up. Through technological advances and partnership, skin disorders can be identified and predicted. PROBLEM: The manual detection of skin diseases may sometimes lead to misclassification due to the same intensity and color levels, which is crucial to the correct diagnosis. SOLUTION: An automated system to identify these skin diseases is applied. An IoT-based skin monitoring infrastructure is imposed that links the entire system. METHOD: In this study, a Retracing-efficient IoT model for identifying the moles, skin tags, and warts using Automatic lumen detection with the help of IoT-based Variation regularity is proposed with the technique imposed IoMT, Automatic lumen detection, Variation regularity, and trigonometric algorithm. RESULTS: The intensity and edge width based on moles, skin tags, and warts edge width heightened intensity accuracy is 56.2% on the image group with image count is 500 to 10000, and the enhanced low-level total sample accuracy is 95.9%. The pixel analysis for intensity with wavelength and intensity with time wavelength is improved from 4.2% to 54.6%, and accuracy is 70.9% formulated. Periodic classification on image count and classification accuracy image count is 87% against the 500 to 10000 image. Correlation performance analysis of lumen detection resolution image pixel and enhanced correlation performance accuracy is 23.50% on the 480 × 640 to 2336 × 3504 pixel images. CONCLUSION: The approach is tested for varying datasets, and comparative analysis is performed that reflects the effectiveness of the proposed system with high accuracy, thus contributing to the development of a perfect platform for skincare to the early detection and diagnosis of skin conditions.

Keywords: Automatic lumen detection, IoT, moles, skin disease, skin medicine, skin tags, warts

DOI: 10.3233/IDA-237442

Journal: Intelligent Data Analysis, vol. 27, no. S1, pp. 161-180, 2023