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

David Liang; David Wang; Alice Wei; Yeseul Choi; Shu Zhang; Marc J. Pomeroy; Perry J. Pickhardt

doi:10.1117/12.2550007

16 March 2020 Performance investigation of deep learning vs. classifier for polyp differentiation via texture features

David Liang, David Wang, Alice Wei, Yeseul Choi, Shu Zhang, Marc J. Pomeroy, Perry J. Pickhardt

Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 113143B (2020) https://doi.org/10.1117/12.2550007
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

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David Liang, David Wang, Alice Wei, Yeseul Choi, Shu Zhang, Marc J. Pomeroy, and Perry J. Pickhardt "Performance investigation of deep learning vs. classifier for polyp differentiation via texture features", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113143B (16 March 2020); https://doi.org/10.1117/12.2550007

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KEYWORDS

Data modeling

Colorectal cancer

Computer aided diagnosis and therapy

Feature extraction

Cancer

Performance modeling

Computed tomography

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