An efficient machine learning approach for the detection of melanoma using dermoscopic images

Diagnosis of dermoscopic skin lesions due to skin cancer is the most challenging task for the experienced dermatologists. In this context, dermoscopy is the non-invasive useful method for the detection of skin lesions which are not visible to naked human eye. Among different types of skin cancers, malignant melanoma is the most aggressive and deadliest form of skin cancer. Its diagnosis is crucial if not detected in early stage. This paper mainly aims to present an efficient machine learning approach for the detection of melanoma from dermoscopic images. It detects melanomic skin lesions based upon their discriminating properties. In first step of proposed method, different types of color and texture features are extracted from dermoscopic images based on distinguished structures and varying intensities of melanomic lesions. In second step, extracted features are fed to the classifier to classify melanoma out of dermoscopic images. Paper also focuses on the role of color and texture features in the context of detection of melanomas. Proposed method is tested on publicly available PH2 dataset in terms of accuracy, sensitivity, specificity and Area under ROC curve (AUC). It is observed that good results are achieved using extracted features, hence proving the validity of the proposed system.