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


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:

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


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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.


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

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Corresponding author

Correspondence to Mustain Billah.

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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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Billah, M., Waheed, S. Minimum redundancy maximum relevance (mRMR) based feature selection from endoscopic images for automatic gastrointestinal polyp detection. Multimed Tools Appl (2020).

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  • Minimum redundancy maximum relevance (mRMR)
  • Video endoscopy
  • Ensemble classifier
  • Feature selection
  • Convolutional Neural Network (CNN)
  • Color Wavelet (CW)
  • Feature extraction