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

Mustain Billah; Sajjad Waheed

doi:10.1007/s11042-020-09151-7

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

Mustain Billah Sajjad Waheed

Year: 2020 Journal: Multimedia Tools and Applications Vol: 79 (33-34)Pages: 23633-23643 Publisher: Springer Science+Business Media

DOI: 10.1007/s11042-020-09151-7

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Keywords:

Computer science Artificial intelligence Feature selection Pattern recognition (psychology) Support vector machine Convolutional neural network Redundancy (engineering) Classifier (UML) Mutual information

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Topics

Colorectal Cancer Screening and Detection

Health Sciences → Medicine → Oncology

Gastrointestinal Bleeding Diagnosis and Treatment

Health Sciences → Medicine → Gastroenterology

Image Retrieval and Classification Techniques

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

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

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