Skin cancer is the most common cancer pathology in the human body and one of the leading causes of death in the world. Artificial intelligence technologies can equal and even surpass the visual classification capabilities of a dermatologist. Thus, it is relevant to develop high-precision intelligent systems for auxiliary diagnostics in the field of dermatology to detect skin cancer in the early stages. The work proposes an ensemble intelligent system for analyzing heterogeneous dermatological data based on multimodal neural networks with various convolutional architectures. The accuracy of the weighted average ensemble model based on multimodal systems using convolutional architectures AlexNet, SeNet_154, Inception_v4, Densenet_161, ResNeXt_50 and ResNeXt_101 for 10 diagnostically significant categories was 87.38%.
Keywords: machine learning, artificial intelligence, convolutional neural networks, multimodal neural networks, ensemble neural networks, digital data processing, heterogeneous data, skin cancer, melanoma