Integration of modified ABCD features and support vector machine for skin lesion types classification

The abnormal growth of skin cells often leads to skin cancer due to the exposure of skin cells to the sun. The skin disease is primarily caused by bacteria, fungus, viruses, UV radiation, and chemical substances. Generally, clinicians have been a trouble to categorize melanoma, seborrheic keratosis and lupus Erythematosus diseases due to the resemblance in the features of pigmented diseases. The paper presents an integrated approach for detecting the skin lesion from the dermoscopic images. The proposed integrated cumulative level difference mean (CLDM) based modified ABCD features and Support vector machine (SVM) have used for the detection and classification of skin lesion images. The proposed modified ABCD features employed for extracting the skin features like shape, size, color, and texture from the skin lesion images. Prior to the classification method, the Eigenvector Centrality feature ranking and selection (ECFS) method has utilized for better classification. After the feature selection method, a skin lesion image is classified by the Support vector machine (SVM). The performance of segmentation has been assessed by evaluating the Jaccard Similarity Index (JSI), Dice similarity coefficient (DSC), sensitivity, specificity, and accuracy. The proposed SVM method classifies the three skin lesion classes and produces excellent classification results with the classification accuracy is 97%, specificity is 98%, sensitivity is 97%, JSI is 97% and DSC is 98% for melanoma, seborrheic keratosis, and lupus Erythematosus respectively. The proposed approach classifies the three skin lesion classes (melanoma, seborrheic keratosis and lupus Erythematosus) with high accuracy. The integrated method not only enhances the accuracy level but also delivers significant information for better classification.