Hierarchical ensemble of multi-level classifiers for diagnosis of Alzheimer's disease

Manhua Liu, Daoqiang Zhang, Pew Thian Yap, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages27-35
Number of pages9
Volume7588 LNCS
DOIs
Publication statusPublished - 2012 Nov 30
Externally publishedYes
Event3rd 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 - Nice, France
Duration: 2012 Oct 12012 Oct 1

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7588 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other3rd 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
CountryFrance
CityNice
Period12/10/112/10/1

Fingerprint

Alzheimer's Disease
Ensemble
Classifiers
Neuroimaging
Classifier
Brain
Imaging
Decode
Pattern Classification
Receiver Operating Characteristic Curve
Small Sample Size
Imaging techniques
Supervised Learning
Classification Algorithm
Supervised learning
Pattern recognition
Transform
Experimental Results

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Liu, M., Zhang, D., Yap, P. T., & Shen, D. (2012). Hierarchical ensemble of multi-level classifiers for diagnosis of Alzheimer's disease. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7588 LNCS, pp. 27-35). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7588 LNCS). https://doi.org/10.1007/978-3-642-35428-1_4

Hierarchical ensemble of multi-level classifiers for diagnosis of Alzheimer's disease. / Liu, Manhua; Zhang, Daoqiang; Yap, Pew Thian; Shen, Dinggang.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7588 LNCS 2012. p. 27-35 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7588 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Liu, M, Zhang, D, Yap, PT & Shen, D 2012, Hierarchical ensemble of multi-level classifiers for diagnosis of Alzheimer's disease. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 7588 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7588 LNCS, pp. 27-35, 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, Nice, France, 12/10/1. https://doi.org/10.1007/978-3-642-35428-1_4
Liu M, Zhang D, Yap PT, Shen D. Hierarchical ensemble of multi-level classifiers for diagnosis of Alzheimer's disease. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7588 LNCS. 2012. p. 27-35. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-35428-1_4
Liu, Manhua ; Zhang, Daoqiang ; Yap, Pew Thian ; Shen, Dinggang. / Hierarchical ensemble of multi-level classifiers for diagnosis of Alzheimer's disease. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7588 LNCS 2012. pp. 27-35 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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