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Minimum redundancy maximum relevance (mRMR) based feature selection from endoscopic images for automatic gastrointestinal polyp detection

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Abstract

In this paper, a computer based system has been proposed as a support to gastrointestinal polyp detection. It can detect and classify gastrointestinal polyps from endoscopic video. Color wavelet (CW) features and convolutional neural network (CNN) features of endoscopic video frames are extracted. Mutual information based feature selection technique-Minimum redundancy maximum relevance (mRMR) is used to scale down feature vector. Instead of using a single classifier, Bootstrap Aggregrating (Bagging)- an ensemble classifier is used. Proposed system has been assessed against different public databases and our own datasets. Evaluation shows that, the system outperforms the existing methods.

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Data Availability

The Endoscopic Video data used to support the findings of this study are available from following websites:

01. http://www.depeca.uah.es/colonoscopy_dataset/ [18].

02. https://polyp.grand-challenge.org/databases/ [3]

Endoscopic videos collected from Lab Aid Hospital, Dhaka can be available upon request to the corresponding author.

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Acknowledgments

The authors are very grateful to Dr. Md. Rabiul Hossain, Gastroenterologist, Liver & Internal Medicine Specialist, Labaid Hospital, Dhaka for his valuable support, suggestions, and consultancy.

Funding

All the research work in this paper has been conducted by self funding.

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Authors and Affiliations

  1. Department of Information and Communication Technology (ICT), Mawlana Bhashani Science and Technology University (MBSTU), Tangail, Bangladesh

    Mustain Billah & Sajjad Waheed

Authors
  1. Mustain Billah
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  2. Sajjad Waheed
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Corresponding author

Correspondence to Mustain Billah.

Ethics declarations

All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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The authors declare that, there is no conflict of interest regarding this paper.

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Billah, M., Waheed, S. Minimum redundancy maximum relevance (mRMR) based feature selection from endoscopic images for automatic gastrointestinal polyp detection. Multimed Tools Appl 79, 23633–23643 (2020). https://doi.org/10.1007/s11042-020-09151-7

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  • DOI: https://doi.org/10.1007/s11042-020-09151-7

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