Skin cancer detection using machine learning techniques with ABCD features

Due to their identical look and lack of distinguishable color variation, detecting skin cancer from dermoscopic images is a difficult process. A better combination of pre-processing and detection methods can make this task comparatively easier and more accurate. For automatic and accurate detection of skin cancer, we have proposed a set of few pre-processing steps like segmentation of skin lesions from background images followed by the ABCD feature extraction for better training of machine learning models like Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF). For this purpose, we have used ISIC 2020 dataset for verification. From this experiment, it is observed that SVM with RBF kernel provided a better detection score of 93.51, whereas 91 %, and 90.67% scores were observed using KNN and RF respectively.