{"title":"基于医学图像的疾病识别系统分析与模型","authors":"Y. Ushenko, D. Uhryn, O. Galochkin, I.V. Zosko","doi":"10.31649/1681-7893-2022-44-2-93-99","DOIUrl":null,"url":null,"abstract":"In given article, we investigate medical images and develop an intelligent system for identification of the disease on their basis. The paper proposes an approach to finding the affected tissue areas in medical images. To find them, a mask was extracted for training a neural network. Mask extraction was carried out using annotations, where polygons with affected tissues were identified. The studied objects were assigned to different classifications of morbidity.","PeriodicalId":142101,"journal":{"name":"Optoelectronic information-power technologies","volume":"64 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"System analysis and model of disease identification based on medical images\",\"authors\":\"Y. Ushenko, D. Uhryn, O. Galochkin, I.V. Zosko\",\"doi\":\"10.31649/1681-7893-2022-44-2-93-99\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In given article, we investigate medical images and develop an intelligent system for identification of the disease on their basis. The paper proposes an approach to finding the affected tissue areas in medical images. To find them, a mask was extracted for training a neural network. Mask extraction was carried out using annotations, where polygons with affected tissues were identified. The studied objects were assigned to different classifications of morbidity.\",\"PeriodicalId\":142101,\"journal\":{\"name\":\"Optoelectronic information-power technologies\",\"volume\":\"64 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Optoelectronic information-power technologies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.31649/1681-7893-2022-44-2-93-99\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optoelectronic information-power technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31649/1681-7893-2022-44-2-93-99","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
System analysis and model of disease identification based on medical images
In given article, we investigate medical images and develop an intelligent system for identification of the disease on their basis. The paper proposes an approach to finding the affected tissue areas in medical images. To find them, a mask was extracted for training a neural network. Mask extraction was carried out using annotations, where polygons with affected tissues were identified. The studied objects were assigned to different classifications of morbidity.