S Sivaraj1, R Malmathanraj1, P Palanisamy1. 1. Department of Electronics and Communication Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India.
Abstract
CONTEXT: Skin cancer is a complex and life-threatening disease caused primarily by genetic instability and accumulation of multiple molecular alternations. AIM: Currently, there is a great interest in the prospects of image processing to provide quantitative information about a skin lesion, that can be relevance for the clinical images and also used as a stand-alone cautioning tool. SETTING AND DESIGN: To accomplish a powerful approach to recognize skin cancer without performing any unnecessary skin biopsies, this article presents a new hybrid technique for the classification of skin images using Firefly with K-Nearest Neighbor algorithm (FKNN). MATERIALS AND METHODS: FKNN classifier is used to predict and classify skin cancer along with threshold-based segmentation and ABCD feature extraction. Image preprocessing and feature extraction techniques are mandatory for any image-based applications. STATISTICAL ANALYSIS USED: Initially, it is essential to eliminate the illumination variation and the other unwanted shadow areas present in the skin image, which is done by homomorphic filtering called preprocessing. RESULTS: The comparison of our proposed method with other existing methods and a comprehensive discussion is explored based on the obtained results. CONCLUSION: The proposed FKNN provides a quantitative information about a skin lesion through hybrid KNN and firefly optimization that helps for recognizing the skin cancer efficiently than other technique with low computational complexity and time.
CONTEXT: Skin cancer is a complex and life-threatening disease caused primarily by genetic instability and accumulation of multiple molecular alternations. AIM: Currently, there is a great interest in the prospects of image processing to provide quantitative information about a skin lesion, that can be relevance for the clinical images and also used as a stand-alone cautioning tool. SETTING AND DESIGN: To accomplish a powerful approach to recognize skin cancer without performing any unnecessary skin biopsies, this article presents a new hybrid technique for the classification of skin images using Firefly with K-Nearest Neighbor algorithm (FKNN). MATERIALS AND METHODS: FKNN classifier is used to predict and classify skin cancer along with threshold-based segmentation and ABCD feature extraction. Image preprocessing and feature extraction techniques are mandatory for any image-based applications. STATISTICAL ANALYSIS USED: Initially, it is essential to eliminate the illumination variation and the other unwanted shadow areas present in the skin image, which is done by homomorphic filtering called preprocessing. RESULTS: The comparison of our proposed method with other existing methods and a comprehensive discussion is explored based on the obtained results. CONCLUSION: The proposed FKNN provides a quantitative information about a skin lesion through hybrid KNN and firefly optimization that helps for recognizing the skin cancer efficiently than other technique with low computational complexity and time.
Entities:
Keywords:
ABCD features; Fuzzy K-Nearest Neighbor classifier; and blue to grayscale conversion; green; homomorphic filtering; preprocessing; red; skin cancer; threshold-based segmentation
Authors: S Rinesh; K Maheswari; B Arthi; P Sherubha; A Vijay; S Sridhar; T Rajendran; Yosef Asrat Waji Journal: J Healthc Eng Date: 2022-02-14 Impact factor: 2.682