Computer-Aided Diagnosis of Alzheimer's Disease Based on Extreme Learning Machine
Lin Weiming1, 2, 3, Yuan Jiangnan1, Feng Chenwei1, Du Min2, 4*
1 School of Opto-Electronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, Fujian, China; 2 College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China; 3 Fujian Provincial Key Laboratory of Medical Instrument and Pharmaceutical Technology, Fuzhou 350108, China; 4 Fujian Provincial Key Laboratory of Eco-industrial Green Technology, Nanping 354300, Fujian, China
Abstract：Alzheimer's disease is a progressive disease of dementia usually associated with brain atrophy. We proposed a method of diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) with the anatomical features of MRI brain images. Method: The data were obtained from ADNI dataset, and the anatomical features of 818 subjects were computed by FreeSurfer software, these features were first preprocessed with age correction algorithm using linear regression to estimate normal aging effect, and was then removed from features. The extreme learning machine was utilized as classifier for diagnosis of AD and MCI with these preprocessed features. The ten-fold cross validation was adopted for calculating accuracy, sensitivity, specificity and area under curve (AUC). Results: By making average with 100 runs, the accuracy of diagnosis of AD was 87.62%, and the AUC reached 94.25%. The accuracy of diagnosis of MCI was 73.38%, and the sensitivity reached 83.88%. The age correction can improve the accuracy of MCI diagnosis. The results demonstrated the efficacy of the proposed method for diagnosis of AD and MCI.
林伟铭, 袁江南, 冯陈伟, 杜民. 基于极限学习机的阿尔兹海默病辅助诊断[J]. 中国生物医学工程学报, 2020, 39(3): 288-294.
Lin Weiming, Yuan Jiangnan, Feng Chenwei, Du Min. Computer-Aided Diagnosis of Alzheimer's Disease Based on Extreme Learning Machine. Chinese Journal of Biomedical Engineering, 2020, 39(3): 288-294.
 Burns A, Iliffe S. Alzheimer's disease[J]. BMJ, 2009, 338(7692):467-471.  Vos T, Allen C, Arora M, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990-2015: A systematic analysis for the Global Burden of Disease Study 2015[J]. Lancet, 2016, 386(10053): 1545-1602.  Du Xiubo, Wang Chao, Liu Qiong. Potential roles of selenium and selenoproteins in the prevention of Alzheimer's disease[J]. Current Topics in Medicinal Chemistry, 2016, 16(8): 835-848.  Desikan RS, Cabral HJ, Hess CP, et al. Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer's disease[J]. Brain, 2009, 132(Pt 8):2048-2057.  Pini L, Pievani M, Bocchetta M, et al. Brain atrophy in Alzheimer's disease and aging[J]. Ageing Research Reviews, 2016, 30:25-48.  Moradi E, Pepe A, Gaser C, et al. Machine learning framework for early MRI-based Alzheimer's conversion prediction in MCI subjects[J]. Neuroimage, 2015, 104:398-412.  Moller C, Pijnenburg YA, Wm VDF, et al. Alzheimer disease and behavioral variant frontotemporal dementia: Automatic classification based on cortical atrophy for single-subject diagnosis[J]. Radiology, 2016, 279(3):838-848.  Cabral C, Morgado PM, Campos CD, et al. Predicting conversion from MCI to AD with FDG-PET brain images at different prodromal stages[J]. Computers in Biology & Medicine, 2015, 58:101-109.  Gray KR, Wolz R, Heckemann RA, et al. Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer's disease[J]. Neuroimage, 2012, 60(1):221-229.  Nir TM, Villalon-Reina JE, Prasad G, et al. Diffusion weighted imaging-based maximum density path analysis and classification of Alzheimer's disease[J]. Neurobiology of Aging, 2015, 36(3):S132-S140.  Selnes P, Aarsland D, Bjrnerud A, et al. Diffusion tensor imaging surpasses cerebrospinal fluid as predictor of cognitive decline and medial temporal lobe atrophy in subjective cognitive impairment and mild cognitive impairment[J]. Journal of Alzheimers Disease, 2013, 33(3):723-736.  Liu Siqi, Liu Sidong, Cai Weidong, et al. Multi-modal neuroimaging feature learning for multi-class diagnosis of Alzheimer's disease[J]. IEEE Trans Biomed Eng, 2015, 62(4):1132-1140.  Tong Tong, Gray K, Gao Qinquan, et al. Multi-modal classification of Alzheimer's disease using nonlinear graph fusion[J]. Pattern Recognition, 2017, 63:171-181.  Rathore S, Habes M, Aksam I M, et al. A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages[J]. Neuroimage, 2017, 155:530-548.  李书通, 肖斌, 李伟生, 等.基于3D-PCANet的阿尔兹海默病辅助诊断[J].计算机科学, 2018, 45(6A):140-142, 156.  卓奕楠, 杨鹏, 邓云, 等.基于多模态典型相关特征表达的阿尔兹海默病诊断[J].中国生物医学工程学报, 2018, 37(1):1-7.  Cuingnet R, Gerardin E, Tessieras J, et al. Automatic classification of patients with Alzheimer's disease from structural MRI: A comparison of ten methods using the ADNI database[J]. Neuroimage, 2011, 56(2):766-781.  Ben AO, Mizotin M, Benois-Pineau J, et al. Alzheimer's disease diagnosis on structural MR images using circular harmonic functions descriptors on hippocampus and posterior cingulate cortex[J]. Computerized Medical Imaging & Graphics, 2015, 44:13-25.  Wyman BT, Harvey DJ, Crawford K, et al. Standardization of analysis sets for reporting results from ADNI MRI data[J]. Alzheimers & Dementia the Journal of the Alzheimers Association, 2013, 9(3):332-337.  Fischl B, Dale A M. Measuring the thickness of the human cerebral cortex from magnetic resonance images[J]. Proc Natl Acad Sci USA, 2000, 97(20):11050-11055.  Giorgio A, Santelli L, Tomassini V, et al. Age-related changes in grey and white matter structure throughout adulthood[J]. Neuroimage, 2010, 51(3):943-951.  Dukart J, Schroeter ML, Mueller K. Age correction in dementia-matching to a healthy brain[J]. PLoS ONE, 2011, 6(7):e22193.  Kim J, Lee B. Automated discrimination of dementia spectrum disorders using extreme learning machine and structural T1 MRI features[J]. Conf Proc IEEE Eng Med Biol Soc, 2017, 2017:1990-1993.  Huang Guangbin, Zhou Hongming, Ding Xiaojian, et al. Extreme learning machine for regression and multiclass classification[J]. IEEE Transactions on Systems Man & Cybernetics Part B, 2012, 42(2):513-529.  Bron EE, Smits M, Wm VDF, et al. Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge[J]. Neuroimage, 2015, 111:562-579.