title: ÐÐ²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¸Ðµ Ð²ÑÑÐ²Ð»ÐµÐ½Ð¸Ðµ Ð¸ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ Ð´ÐµÑÐ¼Ð°ÑÐ¾Ð»Ð¾Ð³Ð¸ÑÐµÑÐºÐ¸Ñ Ð·Ð°Ð±Ð¾Ð»ÐµÐ²Ð°Ð½Ð¸Ð¹ Ð¿Ð¾ ÑÐ¾ÑÐ¾ÑÐ½Ð¸Ð¼ÐºÐ°Ð¼ ÑÐµÐ»Ð¾Ð²ÐµÐºÐ°
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Automatic detection and classification of dermatological diseases based on human photographs
Creators: name: ÐÐµÐ´ÐµÐ½Ð¸ÑÐµÐ², ÐÐ¼Ð¸ÑÑÐ¸Ð¹
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2022
Subjects: Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÐºÐ° ÐºÐ¾Ð¶Ð½ÑÑ Ð·Ð°Ð±Ð¾Ð»ÐµÐ²Ð°Ð½Ð¸Ð¹
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ñ ÐºÐ¾Ð¶Ð¸
ÑÐµÐ³Ð¼ÐµÐ½ÑÐ°ÑÐ¸Ñ ÐºÐ¾Ð¶Ð½ÑÑ Ð·Ð°Ð±Ð¾Ð»ÐµÐ²Ð°Ð½Ð¸Ð¹
ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ñ ÐºÐ¾Ð¶Ð½ÑÑ Ð·Ð°Ð±Ð¾Ð»ÐµÐ²Ð°Ð½Ð¸Ð¹
diagnostics of skin diseases
machine learning
skin segmentation
segmentation of skin diseases
classification of skin diseases

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description: In this paper, the task of identifying and classifying dermatological diseases from human photographs was formulated. To solve the problem, it was divided into subtasks: the task of skin segmentation, the task of segmentation of skin disease, the task of classification of skin disease. The solution of the problem of identification and classification was presented as a sequential application of models, each of which solves the described subtask. Within the framework of solving the problem of skin segmentation, various representations of skin in color spaces were studied, various models and approaches were investigated. A comparative analysis of models with various post-image processing procedures was carried out, as a result of which a skin segmentation model was selected, which was added to the identification and classification process. To solve the problem of segmentation of skin diseases, the search for existing data sets was carried out. Various neural network models developed to solve the problems of medical segmentation were considered. The training of these models on the found data sets was performed. Based on the results of training and test runs, a comparative analysis of the trained models was performed. As a result of the analysis, a model was selected that was included in the overall identification and classification process. In the process of solving the problem of classification of skin diseases, existing datasets were studied. The study of existing neural network models for classification was carried out. A training process was carried out for these models. Based on the results of the comparative analysis, a model was selected to solve the classification problem within the framework of the general process. The models selected as part of the solution of the subtasks were assembled into a single image processing process. A dataset was developed to test this process. When starting the process on this dataset, problems were found related to the classifying model. The classifying model was improved and re-launched on the dataset. The fact of detection and successful recognition of malignant skin diseases using the constructed process was obtained. The result obtained indicates that the constructed process is capable of detecting and recognizing skin diseases in the real world. The software components implemented in the framework of this work can be further used for the early diagnosis of dermatological diseases.