An Effective Semantic Mathematical Model for Skin Cancer Classification Using a Saliency-based Level Set with Improved Boundary Indicator Function.

Skin cancer is one of the most commonly occurring cancer and it causes hundreds to thousands of yearly deaths worldwide. Early identification of skin cancer significantly increases the recovery chances from skin cancer. However, precise skin cancer classification is a challenging task because of the ineffective segmentation of skin cancer. In this paper, the saliency-based level set with an improved boundary indicator function (SLSIBIF) is proposed for the effective segmentation of skin cancer. An improved boundary indicator function is used in the segmentation to detect the skin cancer boundaries even under the constraints of low intensity and illumination. The features from the segmented images are extracted by using the GoogLeNet which uses sparse connections to extract an optimal feature. Further, the classification is done using a multi-class support vector machine (MSVM). The performances of the proposed SLSIBIF-MSVM are evaluated using accuracy, sensitivity, specificity, positive predictive value (PPV), error rate, jacard, and dice coefficient. The existing approaches such as deep-learning system (DLS), ResNet-50, K-means with grasshopper optimization algorithm (GOA) and Region-based CNN (RCNN) and Fuzzy K-means (FKM) are used to compare the SLSIBIF-MSVM. The classification accuracy of SLSIBIF-MSVM for ISIC-2017 dataset is 98.74%, which is high when compared to the DLS, ResNet-50, K means GOA and RCNN-FKM. [ABSTRACT FROM AUTHOR]