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Alzheimer progression classification using fMRI data

fMRI 데이터를 이용한 알츠하이머 진행상태 분류

  • 노주현 (조선대학교 컴퓨터공학과) ;
  • 양희덕 (조선대학교 컴퓨터공학과)
  • Received : 2024.01.19
  • Accepted : 2024.02.08
  • Published : 2024.04.30
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Abstract

The development of functional magnetic resonance imaging (fMRI) has significantly contributed to mapping brain functions and understanding brain networks during rest. This paper proposes a CNN-LSTM-based classification model to classify the progression stages of Alzheimer's disease. Firstly, four preprocessing steps are performed to remove noise from the fMRI data before feature extraction. Secondly, the U-Net architecture is utilized to extract spatial features once preprocessing is completed. Thirdly, the extracted spatial features undergo LSTM processing to extract temporal features, ultimately leading to classification. Experiments were conducted by adjusting the temporal dimension of the data. Using 5-fold cross-validation, an average accuracy of 96.4% was achieved, indicating that the proposed method has high potential for identifying the progression of Alzheimer's disease by analyzing fMRI data.

기능적 자기 공명영상(functional magnetic resonance imaging;fMRI)의 발전은 뇌 기능의 매핑, 휴식 상태에서 뇌 네트워크의 이해에 상당한 기여를 하였다. 본 논문은 알츠하이머의 진행상태를 분류하기 위해 CNN-LSTM 기반의 분류 모델을 제안한다. 첫 번째로 특징 추출 이전 fMRI 데이터에서 잡음을 제거하기 위해 4단계의 전처리를 수행한다. 두 번째, 전처리가 끝나면 U-Net 구조를 활용하여 공간적 특징을 추출한다. 세 번째, 추출된 공간적 특징은 LSTM을 활용하여 시간적 특징을 추출하여 최종적으로 분류하는 과정을 거친다. 실험은 데이터의 시간차원을 조절하여 진행하였다. 5-fold 교차 검증을 사용하여 평균 96.4%의 정확도를 달성하였고 이러한 결과는 제안된 방법이 fMRI 데이터를 분석하여 알츠하이머의 진행을 식별하는데 높은 잠재력을 가지고 있음을 보여준다.

Keywords

Acknowledgement

이 논문은 2022년도 조선대학교 학술연구비의 지원을 받아 연구되었음

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