ABSTRACT. In building robust classifiers for computer-aided detection (CAD) of lesions, selection of relevant features is of fundamental importance.
This paper proposes a feature selection scheme combining AdaBoost with the Minimum Redundancy Maximum Relevance (MRMR) to focus on the most discriminative ...
ABSTRACT. In building robust classifiers for computer-aided detection (CAD) of lesions, selection of relevant features is of fundamental importance.
This paper proposes a feature selection scheme combining AdaBoost with the Minimum Redundancy Maximum Relevance (MRMR) to focus on the most discriminative ...
Jun 11, 2020 · In this paper, a computer based system has been proposed as a support to gastrointestinal polyp detection. It can detect and classify ...
Missing: computer- | Show results with:computer-
Evaluation shows that the proposed computer based system can detect and classify gastrointestinal polyps from endoscopic video and outperforms the existing ...
The genetic algorithm selects subsets of four features, which are later combined to form a committee, with majority vote for classification across the base ...
Missing: MRMR. | Show results with:MRMR.
MRMR is a mutual information-based feature selection scheme (Zhang et al., 2019). This method ranks features according to their relevance to output. This ...
This system automatically segments the colon and identifies polyp candidates using features based on colonic surface curvature. We created a video for each of ...
Minimum redundancy maximum relevance (mRMR) based feature selection from endoscopic images for automatic gastrointestinal polyp detection. Language: English