{"title":"A systematic approach for brain abnormality identification from biomedical images","authors":"Rupal Snehkunj, Ashish Jani","doi":"10.1109/SAPIENCE.2016.7684175","DOIUrl":null,"url":null,"abstract":"Since many years the brain disease has affected many lives. The mortality rate has not reduced despite of consistent efforts have been made to overcome the problems of brain abnormality. Brain abnormalities (Infections, trauma, seizures, and tumors, hemorrhage (stroke) and others) identification from medical images is challenging and time consuming because of manual or semi-automated approaches. The field needs automatic detection systems. The framework proposed in this paper will fulfill the requirement by classifying certain abnormalities which are malignant and benign in nature. Also, the system will assist the radiologist in accurate prediction of the progression of brain abnormalities which will help the society to save many lives.","PeriodicalId":340137,"journal":{"name":"2016 International Conference on Data Mining and Advanced Computing (SAPIENCE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference on Data Mining and Advanced Computing (SAPIENCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SAPIENCE.2016.7684175","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Since many years the brain disease has affected many lives. The mortality rate has not reduced despite of consistent efforts have been made to overcome the problems of brain abnormality. Brain abnormalities (Infections, trauma, seizures, and tumors, hemorrhage (stroke) and others) identification from medical images is challenging and time consuming because of manual or semi-automated approaches. The field needs automatic detection systems. The framework proposed in this paper will fulfill the requirement by classifying certain abnormalities which are malignant and benign in nature. Also, the system will assist the radiologist in accurate prediction of the progression of brain abnormalities which will help the society to save many lives.