An integrated classifier for computer-aided diagnosis of colorectal polyps based on random forest and location index strategies

Yifan Hu; Hao Han; Wei Zhu; Lihong Li; Perry J. Pickhardt; Zhengrong Liang

doi:10.1117/12.2216353

Translator Disclaimer

24 March 2016 An integrated classifier for computer-aided diagnosis of colorectal polyps based on random forest and location index strategies

Yifan Hu, Hao Han, Wei Zhu, Lihong Li, Perry J. Pickhardt, Zhengrong Liang

Author Affiliations +

Proceedings Volume 9785, Medical Imaging 2016: Computer-Aided Diagnosis; 97851L (2016) https://doi.org/10.1117/12.2216353
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

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.

Citation Download Citation

Yifan Hu, Hao Han, Wei Zhu, Lihong Li, Perry J. Pickhardt, and 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); https://doi.org/10.1117/12.2216353

ACCESS THE FULL ARTICLE