An Adaptive Learning Model for Multiscale Texture Features in Polyp Classification via Computed Tomographic Colonography

An Adaptive Learning Model for Multiscale Texture Features in Polyp Classification via Computed Tomographic Colonography

<b>Objective:</b> As an effective lesion heterogeneity depiction, texture information extracted from computed tomography has become increasingly important in polyp classification. However, variation and redundancy among multiple texture descriptors render a challenging task of integratin...

Full description

Bibliographic Details
Main Authors: Weiguo Cao, Marc J. Pomeroy, Shu Zhang, Jiaxing Tan, Zhengrong Liang, Yongfeng Gao, Almas F. Abbasi, Perry J. Pickhardt
Format: Article
Language:English
Published: MDPI AG 2022-01-01
Series:Sensors
Subjects:
colorectal cancer
computed tomographic colonography
polyp classification
texture features
random forest
convolutional neural network
Online Access:https://www.mdpi.com/1424-8220/22/3/907

Internet

https://www.mdpi.com/1424-8220/22/3/907

Similar Items