TY - GEN
T1 - Hierarchical ensemble of multi-level classifiers for diagnosis of Alzheimer's disease
AU - Liu, Manhua
AU - Zhang, Daoqiang
AU - Yap, Pew Thian
AU - Shen, Dinggang
PY - 2012
Y1 - 2012
N2 - Pattern classification methods have been widely studied for analysis of brain images to decode the disease states, such as diagnosis of Alzheimer's disease (AD). Most existing methods aimed to extract discriminative features from neuroimaging data and then build a supervised classifier for classification. However, due to the rich imaging features and small sample size of neuroimaging data, it is still challenging to make use of features to achieve good classification performance. In this paper, we propose a hierarchical ensemble classification algorithm to gradually combine the features and decisions into a unified model for more accurate classification. Specifically, a number of low-level classifiers are first built to transform the rich imaging and correlation-context features of brain image into more compact high-level features with supervised learning. Then, multiple high-level classifiers are generated, with each evaluating the high-level features of different brain regions. Finally, all high-level classifiers are combined to make final decision. Our method is evaluated using MR brain images on 427 subjects (including 198 AD patients and 229 normal controls) from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results show that our method achieves an accuracy of 92.04% and an AUC (area under the ROC curve) of 0.9518 for AD classification, demonstrating very promising classification performance.
AB - Pattern classification methods have been widely studied for analysis of brain images to decode the disease states, such as diagnosis of Alzheimer's disease (AD). Most existing methods aimed to extract discriminative features from neuroimaging data and then build a supervised classifier for classification. However, due to the rich imaging features and small sample size of neuroimaging data, it is still challenging to make use of features to achieve good classification performance. In this paper, we propose a hierarchical ensemble classification algorithm to gradually combine the features and decisions into a unified model for more accurate classification. Specifically, a number of low-level classifiers are first built to transform the rich imaging and correlation-context features of brain image into more compact high-level features with supervised learning. Then, multiple high-level classifiers are generated, with each evaluating the high-level features of different brain regions. Finally, all high-level classifiers are combined to make final decision. Our method is evaluated using MR brain images on 427 subjects (including 198 AD patients and 229 normal controls) from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results show that our method achieves an accuracy of 92.04% and an AUC (area under the ROC curve) of 0.9518 for AD classification, demonstrating very promising classification performance.
UR - http://www.scopus.com/inward/record.url?scp=84869988805&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84869988805&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-35428-1_4
DO - 10.1007/978-3-642-35428-1_4
M3 - Conference contribution
AN - SCOPUS:84869988805
SN - 9783642354274
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 27
EP - 35
BT - Machine Learning in Medical Imaging - Third International Workshop, MLMI 2012, Held in Conjunction with MICCAI 2012, Revised Selected Papers
T2 - 3rd International Workshop on Machine Learning in Medical Imaging, MLMI 2012, Held in conjunction with the 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012
Y2 - 1 October 2012 through 1 October 2012
ER -