Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
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Convergence performance comparison using combination of ML-SVM, PCA, VBM and GMM for detection of AD

알츠하이머 병의 검출을 위한 ML-SVM, PCA, VBM, GMM을 결합한 융합적 성능 비교

  • Alam, Saurar (Department of Information and Communication Engineering, Chosun University) ;
  • Kwon, Goo-Rak (Department of Information and Communication Engineering, Chosun University)
  • Received : 2016.05.26
  • Accepted : 2016.08.08
  • Published : 2016.08.31
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Abstract

Structural MRI(sMRI) imaging is used to extract morphometric features after Grey Matter (GM), White Matter (WM) for several univariate and multivariate method, and Cerebro-spinal Fluid (CSF) segmentation. A new approach is applied for the diagnosis of very mild to mild AD. We propose the classification method of Alzheimer disease patients from normal controls by combining morphometric features and Gaussian Mixture Models parameters along with MMSE (Mini Mental State Examination) score. The combined features are fed into Multi-kernel SVM classifier after getting rid of curse of dimensionality using principal component analysis. The experimenral results of the proposed diagnosis method yield up to 96% stratification accuracy with Multi-kernel SVM along with high sensitivity and specificity above 90%.

구조적 MRI 영상은 여러 단 변량과 다변량 방법을 위해 그레이 메터 (GM), 화이트 메터 (WM), 뇌척수액 (CSF) 세션화 과정을 하고 난후 형태계측학적 특징을 추출하기 위해 사용한다. 새로운 접근 방법은 매우 가벼운 알츠하이머 병에서 가벼운 알츠하이머병의 진단을 위해 적용된다. 간이정신상태검사에 따른 형태계측학적 특징과 가우시안 복합 모델 파라미터를 결합하여 정상인으로부터 알츠하이머 병 환자로 분류하는 방법을 제안한다. 결합한 특징은 주성분 분석 기법을 이용한 고차원의 저주를 제거한 후 다중 커널 SVM 분류기에 공급한다. 제안한 진단 방법의 실험적 결과는 90%이상의 특성도와 고민감도에 따라 다중 커널 SVM을 가진 층화 정확도가 96%까지 최대 산출한다.

Keywords

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