Melanoma Detection Using an Objective System Based on Multiple Connected Neural Networks

Abstract

Melanoma is a common form of skin cancer that dangerously affects many people around the world. Detection of melanoma with the naked eye by dermatologists may be subject to errors. Therefore, the implementation of image processing devices equipped with artificial intelligence can act as a support for the dermatologist in examination and decision making. However, due to the various characteristics of this type of lesions and the presence of noises and artifacts in the images, it is difficult to distinguish melanomas from benign lesions. In this paper, we propose a new type of intelligent system which is based on several neural networks connected on two levels of classification. The first level contains five classifiers (subjective classifiers): the perceptron coupled with color local binary patterns, the perceptron coupled with color histograms of oriented gradients, the generative adversarial network (for segmentation) coupled with ABCD rule, the ResNet, and the AlexNet. They are chosen experimentally and consider the following features of melanomas: texture, shape, color, size, and convolutional pixel connections. At the second level (objective level), one classifier (perceptron-type) decides whether the lesion is a melanoma, based on learning-adjusted weight and the decisions at the first level. The second level is based on back-propagation perceptron that provides the final decision (melanoma or non-melanoma). The subjective and objective levels undergo two separate training phases. This approach allows an easier transition of the system from one database to another. This study shows that the use of the objective classifier brings an accuracy of 97.5% and an F1 score of 97.47%. These results are better than those of the individual classifier and those of the previous literature mentioned in References.

New Trends in Melanoma Detection Using Neural Networks: A Systematic Review

“…Through the appropriate use of several NNs, it is possible to move from subjective classification decisions of individual networks to a decision considered more objective of the global classifier also represented by an NN [6]. In this specific research paper, as can be seen in Figure 28, the authors proposed a system based on a decision taken from multiple NNs.…”

“…The following aspects need to be considered: the number of parameters that need to be analyzed (color, shape, texture, edge, asymmetry, etc. ), the fatigue, and the lack of experience of the specialist [6][7][8]. In most cases, the dermoscopic images are acquired and analyzed by the dermatologist, thus achieving a maximum of 84% examination accuracy (ACC) [9,10], which is insufficient.…”

CU-Net: A New Improved Multi-Input Color U-Net Model for Skin Lesion Semantic Segmentation

“…Skin cancer is divided into three major types, namely, squamous cell carcinoma, basal cell carcinoma, and melanoma [2]. Melanoma is the most dangerous type of skin cancer since it appears and grows in the melanocyte cells that produce melanin [3]- [5]. The main cause of melanoma is not known yet, but it is scientifically proven that direct exposure to ultraviolet rays from sunlight or tanning lamps and beds increases the risk of melanoma.…”

An all‐inclusive computer‐aided melanoma diagnosis based on soft computing

“…[8][9][10] In the last decade, several studies have been performed to identify melanoma using computer image analysis. In this regard, Ichim et al 11 proposed a melanoma diagnosis methodology using multiple connected neural networks. The method is a two-level classification technique.…”