24 March 2016 An integrated classifier for computer-aided diagnosis of colorectal polyps based on random forest and location index strategies
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Abstract
Feature classification plays an important role in differentiation or computer-aided diagnosis (CADx) of suspicious lesions. As a widely used ensemble learning algorithm for classification, random forest (RF) has a distinguished performance for CADx. Our recent study has shown that the location index (LI), which is derived from the well-known kNN (k nearest neighbor) and wkNN (weighted k nearest neighbor) classifier [1], has also a distinguished role in the classification for CADx. Therefore, in this paper, based on the property that the LI will achieve a very high accuracy, we design an algorithm to integrate the LI into RF for improved or higher value of AUC (area under the curve of receiver operating characteristics -- ROC). Experiments were performed by the use of a database of 153 lesions (polyps), including 116 neoplastic lesions and 37 hyperplastic lesions, with comparison to the existing classifiers of RF and wkNN, respectively. A noticeable gain by the proposed integrated classifier was quantified by the AUC measure.
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Yifan Hu, Hao Han, Wei Zhu, Lihong Li, Perry J. Pickhardt, Zhengrong Liang, "An integrated classifier for computer-aided diagnosis of colorectal polyps based on random forest and location index strategies", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97851L (24 March 2016); doi: 10.1117/12.2216353; https://doi.org/10.1117/12.2216353
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