I. Cantillo, A. González, Y. Martínez, I. Bueno, C. García, D. Bueno, V. H. Ortiz
{"title":"基于神经网络的舌组织图像处理与分类图形界面设计","authors":"I. Cantillo, A. González, Y. Martínez, I. Bueno, C. García, D. Bueno, V. H. Ortiz","doi":"10.24254/cnib.18.17","DOIUrl":null,"url":null,"abstract":"In this work, we introduce a graphical interface for detection and classification of different tissue, focusing on tongue soft tissue, based on ADALINE neural networks to provide tools for a highly accurate diagnosis. The interface is capable to identify an affected area or even by exploration of an image of the same sample, to identify normal and pathological conditions. The Adaptive Linear Element (ADALINE) neural network successfully achieved a correct classification of 95% of total study cases, identifying either healthy or abnormal tissue, presented from a set of 70% of images for validations and 30% for training out of the total images.","PeriodicalId":362286,"journal":{"name":"Memorias del Congreso Nacional de Ingeniería Biomédica","volume":"139 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Design of a graphic interface for tongue tissue image processing and classification employing neural networks\",\"authors\":\"I. Cantillo, A. González, Y. Martínez, I. Bueno, C. García, D. Bueno, V. H. Ortiz\",\"doi\":\"10.24254/cnib.18.17\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this work, we introduce a graphical interface for detection and classification of different tissue, focusing on tongue soft tissue, based on ADALINE neural networks to provide tools for a highly accurate diagnosis. The interface is capable to identify an affected area or even by exploration of an image of the same sample, to identify normal and pathological conditions. The Adaptive Linear Element (ADALINE) neural network successfully achieved a correct classification of 95% of total study cases, identifying either healthy or abnormal tissue, presented from a set of 70% of images for validations and 30% for training out of the total images.\",\"PeriodicalId\":362286,\"journal\":{\"name\":\"Memorias del Congreso Nacional de Ingeniería Biomédica\",\"volume\":\"139 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-10-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Memorias del Congreso Nacional de Ingeniería Biomédica\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.24254/cnib.18.17\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Memorias del Congreso Nacional de Ingeniería Biomédica","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.24254/cnib.18.17","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Design of a graphic interface for tongue tissue image processing and classification employing neural networks
In this work, we introduce a graphical interface for detection and classification of different tissue, focusing on tongue soft tissue, based on ADALINE neural networks to provide tools for a highly accurate diagnosis. The interface is capable to identify an affected area or even by exploration of an image of the same sample, to identify normal and pathological conditions. The Adaptive Linear Element (ADALINE) neural network successfully achieved a correct classification of 95% of total study cases, identifying either healthy or abnormal tissue, presented from a set of 70% of images for validations and 30% for training out of the total images.