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

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.