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Proceedings Paper

A deep learning based integration of multiple texture patterns from intensity, gradient and curvature GLCMs in differentiating the malignant from benign polyps
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Paper Abstract

Deep learning such as Convolutional Neural Network (CNN) has demonstrated its superior in the field of image analysis. However, in the medical imaging field, deep learning faces more challenges for tumor classification in computer-aided diagnosis due to uncertainties of lesions including their size, scaling factor, rotation, shapes, etc. Thus, instead of feeding raw images, texture-based CNN model has been designed to classify the objects with their good attributes. For example, gray level co-occurrence matrix (GLCM) can be chosen as the descriptor of the texture pattern for many good properties such as uniform size, shape invariance, scaling invariance. However, there are many different texture metrics to measure the different texture patterns. Thus, an effective and efficient integration model is essential to further improve the classification performance from different texture patterns. In this paper, we proposed a multi-channel texture-based CNN model to effectively integrate intensity, gradient and curvature texture patterns together for differentiating the malignant from benign polyps. Performance was evaluated by the merit of area under the curve of receiver operating characteristics (AUC). Around 0.3~4.8% improvement has been observed by combining different texture patterns together. Finally, classification performance of AUC=86.7% has been achieved for a polyp mass dataset of 87 samples, which obtains 1.8% improvement compared with a state-of-the-art method. The results indicate that texture information from different metrics could be fused and classified with a better classification performance. It also sheds lights that data integration is important and indispensable to pursuit improvement in classification task.

Paper Details

Date Published: 16 March 2020
PDF: 5 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113143D (16 March 2020); doi: 10.1117/12.2550014
Show Author Affiliations
Shu Zhang, Stony Brook Univ. (United States)
Weiguo Cao, Stony Brook Univ. (United States)
Marc Pomeroy, Stony Brook Univ. (United States)
Yongfeng Gao, Stony Brook Univ. (United States)
Jiaxing Tan, Stony Brook Univ. (United States)
Zhengrong Liang, Stony Brook Univ. (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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