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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.5
- 수록면
- 667 - 675 (9page)
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The treatments for symptoms of Alzheimer’s disease are being provided and for the early diagnosis several researches are undergoing. In this regard, by using T1-weighted images several classification techniques had been proposed to distinguish among AD, MCI, and Healthy Control (HC) patients. In this paper, we also used some traditional Machine Learning (ML) approaches in order to diagnose the AD. This paper consists of an improvised feature selection method which is used to reduce the model complexity which accounted an issue while utilizing the ML approaches. In our presented work, combination of subcortical and cortical features of 308 subjects of ADNI dataset has been used to diagnose AD using structural magnetic resonance (sMRI) images. Three classification experiments were performed: binary classification. i.e., AD vs eMCI, AD vs lMCI, and AD vs HC. Proposed Feature Selection method consist of a combination of Principal Component Analysis and Recursive Feature Elimination method that has been used to reduce the dimension size and selection of best features simultaneously. Experiment on the dataset demonstrated that SVM is best suited for the AD vs lMCI, AD vs HC, and AD vs eMCI classification with the accuracy of 95.83%, 97.83%, and 97.87% respectively.
목차
ABSTRACT
1. INTRODUCTION
2. METHODOLOGY
3. EXPERIMENTAL RESULTS
4. DISCUSSION
5. CONCLUSION
REFERENCE
참고문헌 (25)
참고문헌 신청
S.A. Haidar, S. Akihiko, K. Hitoshi, I. Ryuta, I. Manabu, and T. Kenji, “Machine Learning for Diagnosis of AD and Prediction of MCI Progression From Brain MRI Using Brain Anatomical Analysis Using Diffeomorphic Deformation,” Frontiers in Neurology, Vol. 11, pp. 1894, 2021.
E. Lella, N. Amoroso, A. Lombardi, T. Maggipinto, S. Tangaro, R. Bellotti et al., “Communicability Disruption in Alzheimer’s Disease Connectivity Networks,” J ournal of Complex Networks, Vol. 7, pp. 83-100, 2019.
S. ARB Rombouts, F. Barkhof, R. Goekoop, C.J. Stam, and P. Scheltens, “Altered Resting State Networks in Mild Cognitive Impairment and Mild Alzheimer's Disease: an fMRI Study,” Human Brain Mapping, Vol. 26, No. 4 pp. 231-239, 2005.
X. Zhao, Y. Liu, X. Wang, B. Liu, Q. Xi, Q. Guo et al., “Disrupted Small-world Brain Networks in Moderate Alzheimer's Disease: a Resting-State fMRI Study,” PloS One, Vol. 7, No. 3, pp. e33540, 2012.
E. Lella, N. Amoroso, A. Lombardi, T. Maggipinto, S. Tangaro, and R. Bellotti, “Communicability Disruption in Alzheimer’s Disease Connectivity Networks,” Journal of Complex Networks, Vol. 7, Issue 1, pp. 83-100, 2019.
E. Lella, N. Amoroso, A. Lombardi, T. Maggipinto, S. Tangaro, R. Bellotti et al., “Communicability Disruption in Alzheimer’s Disease Connectivity Networks,” J ournal of Complex Networks, Vol. 7, pp. 83-100, 2019.
S. ARB Rombouts, F. Barkhof, R. Goekoop, C.J. Stam, and P. Scheltens, “Altered Resting State Networks in Mild Cognitive Impairment and Mild Alzheimer's Disease: an fMRI Study,” Human Brain Mapping, Vol. 26, No. 4 pp. 231-239, 2005.
X. Zhao, Y. Liu, X. Wang, B. Liu, Q. Xi, Q. Guo et al., “Disrupted Small-world Brain Networks in Moderate Alzheimer's Disease: a Resting-State fMRI Study,” PloS One, Vol. 7, No. 3, pp. e33540, 2012.
E. Lella, N. Amoroso, A. Lombardi, T. Maggipinto, S. Tangaro, and R. Bellotti, “Communicability Disruption in Alzheimer’s Disease Connectivity Networks,” Journal of Complex Networks, Vol. 7, Issue 1, pp. 83-100, 2019.
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