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