Fully Automated Approach for Early Detection of Pigmented Skin Lesion Diagnosis Using ABCD.

Computerized analysis of pigmented skin lesions (PSLs) is a lively space of survey that dates back over 25 years. Recently, different automated computer-based systems stand to be a helpful tool. Physicians' usage for ABCD worldwide as the main tool of diagnosis and self-examination make it the common reference for different skin cancer diagnosis models. This system is comprised of the main four key warning signs of the ABCD model that can be detected by visual inspection and more accurately identified by the automated system to diagnose melanoma. Based on the image area identified as PSL, through pre-processing and segmentation step, the features will then be detected regarding ABCD rule. According to what ABCD stands for, the proposed study extracts Asymmetry, Border and Color features, in addition to various parameters introduce parameter "D." Finally, as the worldwide definition of ABCD rule of cancer diagnoses was discussed, this research also makes the final decision according to the Total Dermoscopic Score (TDS) Index, in addition to another three popular machine learning classifiers. ANN, SVM, and K-nearest neighbor were used for classification of the segmented lesions in addition to the traditional TDS. This research shows perfect results for calculating the ABCD score automatically, which reflects its viability. Different experiments developed in regard to features variety and different classification methods to reach 98.1%, 95%, and 98.75% classification accuracy when dermoscopic images were classified by TDS, Automatic ANN, and linear SVM, respectively, where the clinical images reached perfect accuracy 100% when classified by linear SVM, and very promising result 98.75% as per automatic ANN. This system considered to be the first promising digitalized system for traditional TDS regarding the achieved accuracy and using of a simple Graphical User Interface (GUI) to facilitate user easy use. © Springer Nature Switzerland AG 2020.