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Melanoma skin cancer is the most life-threatening and fatal disease in the family of skin cancer diseases. Detecting melanoma at its early stage can improvise survival which requires an effective classification technique to categorize dermoscopic images as melanoma and non-melanoma. Based on the quality of the extracted features, the classification accuracy of the classifier is determined. However, the classification accuracy of the existing approaches is poor due to the presence of improper image boundaries and low quality. To overcome the fore-mentioned issues, this research introduced an optimization-based feature selection approach using the modified golden jackal optimization (MGJO) algorithm. The pre-processed image is segmented using a semantic mathematical model known as saliency-based level set with improved boundary indicator function (SLSIBIF) and the feature extraction is performed using the GoogleNet architecture. After this, the proposed MGJO algorithm was used to select the relevant features which aid in precise classification performed using multiclass-support vector machine (MSVM). The obtained results show that the proposed MGJO-MSVM achieves enhanced classification accuracy of 98.89 % for the ISIC-2017 dataset whereas the accuracy of the existing feature adaptive transformer network (FAT-NET), multi-attention fusion convolutional neural network-based skin cancer diagnosis (MAFCNN-SCD), W-net inception residual network, region-based convolutional neural network with fuzzy k-means clustering (RCNN-FKM) and gated fusion attention network is 93.26%, 92.22%, 96.97%, 95.6% and 93.97% respectively. [ABSTRACT FROM AUTHOR]

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]