Lesion analysis towards melanoma detection using soft computing techniques

Abstract Introduction Melanoma has been increasing worldwide. An efficient method based on bio-inspired algorithms and neural networks has been suggested in this study. Objective The main goal of this study is to reduce the complexity of classifier using feature selection method thereby reducing time for classification and to balance specificity and sensitivity. Materials and methods The approach to the problem has been divided into three basic steps; lesion segmentation feature extraction and the last step is classification. Segmentation and feature extraction was performed using image processing techniques. A novel fitness function has been proposed that will be optimized using Binary Bat Algorithm (BBA) to obtain the most relevant feature set. Result Support Vector Machine (SVM) and Radial Basis Function Network (RBFN) were used for classification process. SVM and RBFN produced accuracy of 87% and 91% respectively for K10 protocol. Specificity and Sensitivity for SVM in K10 protocol was obtained to be 82% and 92% respectively. As for RBFN specificity and sensitivity was obtained to be 90% and 93% respectively. We were able to obtain balance between specificity and sensitivity through our approach. Conclusion With simple network structure like RBFN and SVM we were able to obtain results better than other complex networks.