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Data and feature mixed ensemble based extreme learning machine for medical object detection and segmentation

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Abstract

Extreme learning machine (ELM) is a single-hidden layer feed-forward neural network with an efficient learning algorithm. Conventionally an ELM is trained using all the data based on the least square solution, and thus it may suffer from overfitting. In this paper, we present a new method of data and feature mixed ensemble based extreme learning machine (DFEN-ELM). DFEN-ELM combines data ensemble and feature subspace ensemble to tackle the overfitting problem and it takes advantage of the fast speed of ELM when building ensembles of classifiers. Both one-class and two-class ensemble based ELM have been studied. Experiments were conducted on computed tomography (CT) data for liver tumor detection and segmentation as well as magnetic resonance imaging (MRI) data for rodent brain segmentation. To improve the ensembles with new training data, sequential kernel learning is adopted further in the experiments on CT data for speedy retraining and iteratively enhancing the image segmentation performance. Experiment results on different testing cases and various testing datasets demonstrate that DFEN-ELM is a robust and efficient algorithm for medical object detection and segmentation.

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Acknowledgments

We wish to acknowledge the funding support for this project from Nanyang Technological University under the Undergraduate Research Experience on CAmpus (URECA) programme and the funding support by Agency for Science, Technology and Research (A*STAR) Joint Council Office under DP Grant 1334 k00084.

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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, 50 Nanyang Avenue, 639798, Singapore

    Wanzheng Zhu, Zhiping Lin & Yongzhong Yang

  2. Institute for Infocomm Research, 1 Fusionopolis Way, #21-01, Connexis, 138632, Singapore

    Weimin Huang, Su Huang & Jiayin Zhou

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  1. Wanzheng Zhu
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  2. Weimin Huang
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  3. Zhiping Lin
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  5. Su Huang
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  6. Jiayin Zhou
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Correspondence to Weimin Huang.

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Zhu, W., Huang, W., Lin, Z. et al. Data and feature mixed ensemble based extreme learning machine for medical object detection and segmentation. Multimed Tools Appl 75, 2815–2837 (2016). https://doi.org/10.1007/s11042-015-2582-9

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  • DOI: https://doi.org/10.1007/s11042-015-2582-9

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