Computer-aided skin cancer diagnosis based on a New meta-heuristic algorithm combined with support vector method

Abstract Skin cancer has grown significantly over the past decades, and the importance of its initial treatment is increasing day by day. This study aims to use a computer-aided diagnosis automatic system for the exact diagnosis of cancerous cases. In the present study, after applying pre-processing operations including contrast enhancement, image thresholding, and mathematical morphology for highlighting the important areas, feature extraction based on 20 different features has been applied. Then, for decreasing the system complexity, a new improved version of the world cup optimization algorithm is utilized for the features pruning. After optimal selection of the features, the injected into a support vector machine as a classifier to determine the cancerous areas. Experimental results are compared by different new algorithms including the classic SVM method, hybrid optimized neural networks based on gray wolf optimizer and particle swarm optimization algorithm, and also convolutional neural network. Results obtained for skin lesions images from two different databases indicate that the proposed method has higher efficiency than other published methods, demonstrating even better performance than the well-known classification method, the convolutional neural networks. The proposed method presents the highest correct detection rate - 92.64 for ACS images and 87.5 for ISIC images with very low false acceptance rates (4.41 % and 9 % for ACS and ISIC images, respectively) and even less false rejection rates, this is, 2.94 % on ACS images and 3.5 % on ISIC images. These results prove that the proposed method may be reliably used to diagnose skin cancer through dermoscopy images.