Melanoma Detection and Classification using Computerized Analysis of Dermoscopic Systems: A Review

Abstract

Malignant melanoma is considered as one of the most deadly cancers, which has broadly increased worldwide since the last decade. In 2018, around 91,270 cases of melanoma were reported and 9,320 people died in the US. However, diagnosis at the initial stage indicates a high survival rate. The conventional diagnostic methods are expensive, inconvenient and subject to the dermatologist’s expertise as well as a highly equipped environment. Recent achievements in computerized based systems are highly promising with improved accuracy and efficiency. Several measures such as irregularity, contrast stretching, change in origin, feature extraction and feature selection are considered for accurate melanoma detection and classification. Typically, digital dermoscopy comprises four fundamental image processing steps including preprocessing, segmentation, feature extraction and reduction, and lesion classification. Our survey is compared with the existing surveys in terms of preprocessing techniques (hair removal, contrast stretching) and their challenges, lesion segmentation methods, feature extraction methods with their challenges, features selection techniques, datasets for the validation of the digital system, classification methods and performance measure. Also, a brief summary of each step is presented in the tables. The challenges for each step are also described in detail, which clearly indicate why the digital systems are not performing well. Future directions are also given in this survey.