Identification of Suspicious Naevi in Dermoscopy Images by Learning Their Appearance

Naevi are benign skin lesions that appear on the skin surface as small brown, tan, or pink spots. These are important to monitor, as a high number of naevi is the strongest known phenotypic risk factor for melanoma, and about a third of melanoma are thought to derive directly from a precursor naevus. The main aim of this research is to model the dermoscopic naevus appearance using machine learning and characterise their features by classifying them as suspicious or non-suspicious naevi. To extract the prominent appearance features of naevi, principal component analysis (PCA) along with convolutional autoencoder (CAE) were implemented for automated feature extraction. These features were then used for the classification of naevi using random forest (RF) and artificial neural network (ANN) classifiers. Using the features extracted by CAE, ANN achieved high average accuracy, specificity, sensitivity, precision, and AUC of 95.62%, 91.24%, 100%, 91.95%, 95.6% respectively. In addition, RF showed that both PCA and CAE based methods had an overall accuracy of 88.46%. Moreover, RF used to rank the features which helped in selecting the most important features useful for naevus classification. If validated clinically, machine learning (ML) approaches might be an efficient guide in early detection of melanoma by identifying the suspicious naevi clinicians need to assess carefully.