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Proceedings Paper

Performance investigation of deep learning vs. classifier for polyp differentiation via texture features
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Paper Abstract

Computer-aided diagnosis (CADx) of polyps is essential for advancing computed tomography colonography (CTC) with diagnostic capability. In this paper, we present a study of investigating the performance between deep learning and Random Forest (RF) classifier for polyp differentiation in CTC. First, we conducted feature extraction via an extended Haralick model (eHM) to build a total of 30 texture features. The gray level co-occurrence matrix (GLCM) is generated to encode 3D CT image information into a 2D matrix as input to the convolutional neural network (CNN). Then, we split the polyp classification into two state-of-the-art frameworks: the eHM texture features/RF and the GLCM texture matrices/CNN. We evaluated their performances by the merit of area under the curve of receiver operating characteristic using 1,278 polyps (confirmed by pathology). Results demonstrated that by balancing the data, both CNN model and RF classifier can learn or analyze features effectively, and achieve high performance. RF classifier in general outperformed CNN model with a gain of 6.4% (balanced datasets) and 5.4% (unbalanced datasets), showing its effective in feature extraction and analysis for polyp differentiation. However, the performance of CNN got improved through the addition of new data with a gain of 3.6% (balanced datasets) and 3.4% (unbalanced datasets), whereas RF classifier showed no gain when we enlarged datasets. This demonstrated that CNN model have the potential to improve the classification task performance when dealing with larger dataset. This study provided valuable information on how to design experiments to improve CADx of polyps.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113143B (16 March 2020); doi: 10.1117/12.2550007
Show Author Affiliations
David Liang, Stony Brook Univ. (United States)
Ward Melville High School (United States)
David Wang, Stony Brook Univ. (United States)
Syosset High School (United States)
Alice Wei, Stony Brook Univ. (United States)
Staten Island Technical High School (United States)
Yeseul Choi, Stony Brook Univ. (United States)
Syosset High School (United States)
Shu Zhang, Stony Brook Univ. (United States)
Marc J. Pomeroy, Stony Brook Univ. (United States)
Perry J. Pickhardt, Univ. of Wisconsin-Madison (United States)

Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

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