Development and validation of an artificial neural network to support the diagnosis of melanoma from dermoscopic images

Abstract

Introduction: With the advancement of digital image analysis, predictive analysis, and machine learning methods, studies have emerged regarding the use of artificial intelligence in imaging tests such as dermoscopy. Objective: Construction, testing, and implementation of an artificial neural network based on characteristics of dermoscopic images. Methods: 1949 images of melanocytic nevi and melanomas were included, both from the authors’ files and from dermoscopic image banks available on the internet, and routines and plugins were developed to extract 58 features applied to a multilayered neural network construction algorithm. Also, 52 dermatologists assessed 40 random images and compared the results compared. Results: The training and testing of the neural network obtained a correct percentage of classification of 78.5% and 79.1%, respectively, with a ROC curve covering 86.5% of the area. The sensitivity and specificity of dermatologists were 71.8% and 52%. For the same images and a cutoff point of 0.4 (40%) of the output value, the application obtained 62% and 56% values, respectively Conclusions: Multilayer neural network models can assist in the dermoscopic evaluation of melanocytic nevi and melanomas regarding the differential diagnosis between them.