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http://dx.doi.org/10.23087/jkicsp.2022.23.4.011

SCLC-Edge Detection Algorithm for Skin Cancer Classification  

June-Young Park (Department of Convergence Healthcare Medicine, Graduate School of Ajou University)
Chang-Min Kim (Department of Information Communication Software Engineering, Sangji University)
Roy C. Park (Department of Information Communication Software Engineering, Sangji University)
Publication Information
Journal of the Institute of Convergence Signal Processing / v.23, no.4, 2022 , pp. 256-263 More about this Journal
Abstract
Skin cancer is one of the most common diseases in the world, and the incidence rate in Korea has increased by about 100% over the past five years. In the United States, more than 5 million people are diagnosed with skin cancer every year. Skin cancer mainly occurs when skin tissue is damaged for a long time due to exposure to ultraviolet rays. Melanoma, a malignant tumor of skin cancer, is similar in appearance to Atypical melanocytic nevus occurring on the skin, making it difficult for the general public to be aware of it unless secondary signs occur. In this paper, we propose a skin cancer lesion edge detection algorithm and a deep learning model, CRNN, which performs skin cancer lesion classification for early detection and classification of these skin cancers. As a result of the experiment, when using the contour detection algorithm proposed in this paper, the classification accuracy was the highest at 97%. For the Canny algorithm, 78% was shown, 55% for Sobel, and 46% for Laplacian.
Keywords
Deep Learning; Medical Image; Edge Detection; Image Classification; AI;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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