Automated Detection of Alzheimer's Disease Using Brain MRI Images– A Study with Various Feature Extraction Techniques.
The aim of this work is to develop a Computer-Aided-Brain-Diagnosis (CABD) system that can determine if a brain scan shows signs of Alzheimer's disease. The method utilizes Magnetic Resonance Imaging (MRI) for classification with several feature extraction techniques. MRI is a non-invasive procedure...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 9 |
|---|---|
| Autores principales: | , , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Sep2019
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=138200114&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 138200114 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Sep2019 vid: 43 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 138200114 138200114 138200114 10.1007/s10916-019-1428-9 138200114 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automated Detection of Alzheimer's Disease Using Brain MRI Images– A Study with Various Feature Extraction Techniques. aug: au: Acharya, U. Rajendra Fernandes, Steven Lawrence WeiKoh, Joel En Ciaccio, Edward J. Fabell, Mohd Kamil Mohd Tanik, U. John Rajinikanth, V. Yeong, Chai Hong affil: Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, Singapore sug: subj: Alzheimer's Disease Diagnosis Brain Pathology Magnetic Resonance Imaging Methods Automation, Laboratory Human Image Processing, Computer Assisted T-Tests Comparative Studies Sensitivity and Specificity Equipment Reliability Experimental Studies Machine Learning Technology, Medical Deep Learning ab: The aim of this work is to develop a Computer-Aided-Brain-Diagnosis (CABD) system that can determine if a brain scan shows signs of Alzheimer's disease. The method utilizes Magnetic Resonance Imaging (MRI) for classification with several feature extraction techniques. MRI is a non-invasive procedure, widely adopted in hospitals to examine cognitive abnormalities. Images are acquired using the T2 imaging sequence. The paradigm consists of a series of quantitative techniques: filtering, feature extraction, Student's t-test based feature selection, and k-Nearest Neighbor (KNN) based classification. Additionally, a comparative analysis is done by implementing other feature extraction procedures that are described in the literature. Our findings suggest that the Shearlet Transform (ST) feature extraction technique offers improved results for Alzheimer's diagnosis as compared to alternative methods. The proposed CABD tool with the ST + KNN technique provided accuracy of 94.54%, precision of 88.33%, sensitivity of 96.30% and specificity of 93.64%. Furthermore, this tool also offered an accuracy, precision, sensitivity and specificity of 98.48%, 100%, 96.97% and 100%, respectively, with the benchmark MRI database. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|