A novel melanoma detection model: adapted K-means clustering-based segmentation process

Abstract

Abstract Objectives The main intention of this paper is to propose a new Improved K-means clustering algorithm, by optimally tuning the centroids. Methods This paper introduces a new melanoma detection model that includes three major phase’s viz. segmentation, feature extraction and detection. For segmentation, this paper introduces a new Improved K-means clustering algorithm, where the initial centroids are optimally tuned by a new algorithm termed Lion Algorithm with New Mating Process (LANM), which is an improved version of standard LA. Moreover, the optimal selection is based on the consideration of multi-objective including intensity diverse centroid, spatial map, and frequency of occurrence, respectively. The subsequent phase is feature extraction, where the proposed Local Vector Pattern (LVP) and Grey-Level Co-Occurrence Matrix (GLCM)-based features are extracted. Further, these extracted features are fed as input to Deep Convolution Neural Network (DCNN) for melanoma detection. Results Finally, the performance of the proposed model is evaluated over other conventional models by determining both the positive as well as negative measures. From the analysis, it is observed that for the normal skin image, the accuracy of the presented work is 0.86379, which is 47.83% and 0.245% better than the traditional works like Conventional K-means and PA-MSA, respectively. Conclusions From the overall analysis it can be observed that the proposed model is more robust in melanoma prediction, when compared over the state-of-art models.