Selection of a neural network model for early detection of skin melanoma

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Abstract
The approach to diagnosis and treatment in dermatovenerology has changed significantly with the introduction of new technologies and inventions. Computer algorithms have been used to assist dermatovenerologists in diagnosing diseases, including malignant melanoma of the skin. Instrumental methods for diagnosing skin melanoma, based on the processing of dermatoscopic images, can be improved by using artificial intelligence. Machine learning is a subset of artificial intelligence in which computer programs automatically learn from experience without explicit operation instructions. The most promising direction is deep learning of artificial neural networks. In this paper, several models of artificial neural networks were studied to assess the prospects for their use for the early diagnosis of skin melanoma. The efficacy of the various models was analyzed using the HAM10000 dataset image classification. Models of convolutional neural networks, pre-trained neural networks with transfer learning, and the “soft attention” mechanism were evaluated: Xception, ResNet50, RAN50, SEnet50, ARL-CNN50, Inception Resnet V2 — IRv2 12x12+SA, IRv2 5x5+SA. The results showed that the most promising model was Inception Resnet V2 with the addition of a «soft attention» mechanism.

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