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

2020 ◽  
Vol 79 (33-34) ◽  
pp. 23633-23643
Author(s):  
Mustain Billah ◽  
Sajjad Waheed
Keyword(s):  
Feature Selection ◽  
Polyp Detection ◽  
Endoscopic Images ◽  
Minimum Redundancy Maximum Relevance

Multi-Classifier-Based Automatic Polyp Detection in Endoscopic Images

2016 ◽  
Vol 36 (6) ◽  
pp. 871-882 ◽  
Author(s):  
Yeong-Jun Cho ◽  
Seung-Hwan Bae ◽  
Kuk-Jin Yoon
Keyword(s):  
Polyp Detection ◽  
Endoscopic Images

AN EFFICIENT FEATURE SELECTION ALGORITHM FOR COMPUTER-AIDED POLYP DETECTION

2006 ◽  
Vol 15 (06) ◽  
pp. 893-915 ◽  
Author(s):  
JIANG LI ◽  
JIANHUA YAO ◽  
RONALD M. SUMMERS ◽  
NICHOLAS PETRICK ◽  
MICHAEL T. MANRY ◽  
...  
Keyword(s):  
Feature Selection ◽  
Cross Validation ◽  
Search Algorithm ◽  
Piecewise Linear ◽  
Least Square ◽  
Support Vector ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Polyp Detection ◽  
Computer Aided

We present an efficient feature selection algorithm for computer aided detection (CAD) computed tomographic (CT) colonography. The algorithm (1) determines an appropriate piecewise linear network (PLN) model by cross validation, (2) applies the orthonormal least square (OLS) procedure to the PLN model utilizing a Modified Schmidt procedure, and (3) uses a floating search algorithm to select features that minimize the output variance. The undesirable "nesting effect" is prevented by the floating search approach, and the piecewise linear OLS procedure makes this algorithm very computationally efficient because the Modified Schmidt procedure only requires one data pass during the whole searching process. The selected features are compared to those obtained by other methods, through cross validation with support vector machines (SVMs).

scholarly journals Feature selection for computer-aided polyp detection using MRMR

2010 ◽  
Author(s):  
Xiaoyun Yang ◽  
Boray Tek ◽  
Gareth Beddoe ◽  
Greg Slabaugh
Keyword(s):  
Feature Selection ◽  
Polyp Detection ◽  
Computer Aided ◽  
Selection For
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