Application of classical neural networks for malignant skin lesions recognition on digital skin images
Abstract
Application of classical neural networks for malignant skin lesions recognition on digital skin images
Incoming article date: 25.04.2021The purpose of this research is analysis of the possibility of using classical neural networks for the malignant neoplasms recognition on skin digital images. In this study was used International Skin Imaging Collaboration database including 6594 dermatoscopic images. At the first stage of the study, digital skin images were classified into malignant and benign neoplasms using the IBM SPSS Statistic tool to automatically select the architecture for artificial neural network. At the second stage was built architecture of artificial neural network, which include one hidden layer. At the third stage was used architecture of an artificial neural network with two hidden layers. In the course of the study digital skin images classified with the highest value of the accuracy indicator (0,752 [0,736 ; 0,768]) during classification using the architecture of an artificial neural network, which includes two hidden layers. The sigmoid was used as the activation function for the hidden layers. The hyperbolic tangent was used as activation function for output layer. With this value of the accuracy, the specificity of the diagnostic method was obtained – 0,813 [0,802 ; 0,824], as well as the value of the sensitivity – 0,665 [0,637 ; 0,691]. Thus, artificial neural networks can be used as a method for skin malignant neoplasms diagnostic on digital images.
Keywords: artificial neural networks, digital skin images, machine learning, image classification, skin cancer