A novel optimized neutrosophic k-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(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(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(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(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 +/- 1.61% average accuracy compared with k-means and gamma-k-means methods.