A Quaternary Classifier for the Clinical Evaluation of Pigmented Skin Lesions

This study reports results of a pilot study, in which pigmented skin lesions are automatically classified into four classes: benign, dysplastic nevus with mild atypia, dysplastic nevus with severe atypia, and melanoma. The pilot study enrolled subjects from dermatology clinic at Baylor University Medical Center at Dallas from June 2016 to August 2017. 30 high-quality dermoscopic images were randomly selected from an image bank of 96 to obtain a statistically balanced dataset. Melanoma samples were histologically verified. A dermoscopy-based automated image analyzer with quaternary classification of pigmented skin lesions was proposed. The image analyzer automatically extracts five lesion features, most used in clinical practise, applying an active contour, and pairwise classification employing six Support Vector Machines. The pairwise accuracy of classifications are reported between 92% and 94% and used to determine the corresponding confidence intervals. Through the pairwise classification results maximum hits and acyclic tree decisions were utilized to reach the final classification of a lesion. Using leave-one-out validation, accuracy of the quaternary dermoscopy-based image analyzer were determined as 90% using the histopathologic diagnoses as the ground truth. This novel, dermoscopy-based image classifier accurately classifies pigmented skin lesions small data-sets into benign, two types of dysplastic nevi, and malignant lesions. To the best of our knowledge, there is no other automated skin lesion classification framework, in the literature, which distinguishes between different nevus lesion.