A novel optimized neutrosophic <Emphasis Type="Italic">k</Emphasis>-means using genetic algorithm for skin lesion detection in dermoscopy images

This paper implemented a new skin lesion detection method based on the genetic algorithm (GA) for optimizing the neutrosophic set (NS) operation to reduce the indeterminacy on the dermoscopy images. Then, k-means clustering is applied to segment the skin lesion regions. Therefore, the proposed method is called optimized neutrosophic k-means (ONKM). On the training images set, an initial value of \(\alpha \) in the \(\alpha \)-mean operation of the NS is used with the GA to determine the optimized \(\alpha \) value. The Jaccard index is used as the fitness function during the optimization process. The GA found the optimal \(\alpha \) in the \(\alpha \)-mean operation as \(\alpha _{\mathrm{optimal}} =0.0014\) in the NS, which achieved the best performance using five fold cross-validation. Afterward, the dermoscopy images are transformed into the neutrosophic domain via three memberships, namely true, indeterminate, and false, using \(\alpha _{\mathrm{optimal}}\). The proposed ONKM method is carried out to segment the dermoscopy images. Different random subsets of 50 images from the ISIC 2016 challenge dataset are used from the training dataset during the fivefold cross-validation to train the proposed system and determine \(\alpha _{\mathrm{optimal}}\). Several evaluation metrics, namely the Dice coefficient, specificity, sensitivity, and accuracy, are measured for performance evaluation of the test images using the proposed ONKM method with \(\alpha _{\mathrm{optimal}} =0.0014\) compared to the k-means, and the \(\gamma \)–k-means methods. The results depicted the dominance of the ONKM method with \(99.29\pm 1.61\%\) average accuracy compared with k-means and \(\gamma \)–k-means methods.