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3D-GLCM CNN: A 3-dimensional gray-level co-occurrence matrix based CNN model for polyp classification via CT colonography

Accurately classifying colorectal polyps, or differentiating malignant from benign ones, has a significant clinical impact on early detection and identifying optimal treatment of colorectal cancer. Convolution neural network (CNN) has shown great potential in recognizing different objects (e.g. huma...

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Опубликовано в: :	IEEE Trans Med Imaging
Главные авторы:	Tan, Jiaxiang, Gao, Yongfeng, Liang, Zhengrong, Cao, Weiguo, Pomeroy, Marc J., Huo, Yumei, Li, Lihong, Barish, Matthew A., Abbasi, Almas F., Pickhardt, Perry J.
Формат:	Artigo
Язык:	Inglês
Опубликовано:	2019
Предметы:	Article
Online-ссылка:	https://ncbi.nlm.nih.gov/pmc/articles/PMC7269812/ https://ncbi.nlm.nih.gov/pubmed/31899419 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/TMI.2019.2963177
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3D-GLCM CNN: A 3-dimensional gray-level co-occurrence matrix based CNN model for polyp classification via CT colonography

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