F. E. El-Gamal, M. Elmogy, A. Khalil, M. Ghazal, Hassan H. Soliman, A. Atwan, R. Keynton, G. Barnes, A. El-Baz
{"title":"A Local/Regional Based CAD System for Early Diagnosis of Alzheimer's Disease Using sMRI Scans","authors":"F. E. El-Gamal, M. Elmogy, A. Khalil, M. Ghazal, Hassan H. Soliman, A. Atwan, R. Keynton, G. Barnes, A. El-Baz","doi":"10.1109/IST48021.2019.9010162","DOIUrl":null,"url":null,"abstract":"Alzheimer's disease (AD) is one of the neurodegenerative diseases, an irreparable one, that targets the central nervous system and causes dementia. During its progression, the disease goes through a number of stages where the diagnosis of the disease at its early stage is highly recommended. Despite this recommendation, accomplishing this diagnosis task faces a number of obstacles including the variable impact of the disease on its sufferers. This paper mainly aims to assist in the early diagnosis process of AD through introducing a brain regional based computer-aided diagnosis (CAD) system, using structural magnetic resonance imaging (sMRI), that goes through four main stages: preprocessing, brain labeling, extracting the discriminant features, as well as diagnosing. The novelty of the our work is to offer a local/regional diagnosis to serve the subject-dependent effect of the disease followed by a global diagnosis with an overall promising performance as evaluated with the related work. The experimental results show accuracy of 96.6%, specificity of 100%, and sensitivity of 94.25%. Validating our system with the related work and some well-known classifiers shows promising results in addressing this research point.","PeriodicalId":117219,"journal":{"name":"2019 IEEE International Conference on Imaging Systems and Techniques (IST)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Conference on Imaging Systems and Techniques (IST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IST48021.2019.9010162","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Alzheimer's disease (AD) is one of the neurodegenerative diseases, an irreparable one, that targets the central nervous system and causes dementia. During its progression, the disease goes through a number of stages where the diagnosis of the disease at its early stage is highly recommended. Despite this recommendation, accomplishing this diagnosis task faces a number of obstacles including the variable impact of the disease on its sufferers. This paper mainly aims to assist in the early diagnosis process of AD through introducing a brain regional based computer-aided diagnosis (CAD) system, using structural magnetic resonance imaging (sMRI), that goes through four main stages: preprocessing, brain labeling, extracting the discriminant features, as well as diagnosing. The novelty of the our work is to offer a local/regional diagnosis to serve the subject-dependent effect of the disease followed by a global diagnosis with an overall promising performance as evaluated with the related work. The experimental results show accuracy of 96.6%, specificity of 100%, and sensitivity of 94.25%. Validating our system with the related work and some well-known classifiers shows promising results in addressing this research point.