Colorectal cancer tissue classification based on machine learning

Min Jen Tsai, Imam Yuadi, Yu Han Tao

研究成果: Paper同行評審

2 引文 斯高帕斯(Scopus)

摘要

For digital pathology, automatic recognition of different tissue types in histological images is important for diagnostic assistance and healthcare. Since histological images generally contain more than one tissue type, multi-class texture analysis plays a critical role to solve this problem. This study examines the important statistical features including Gray Level Co-occurrence Matrix (GLCM), Discrete Wavelet Transform (DWT), Spatial filters, Wiener filter, Gabor filters, Haralick features, fractal filters, and local binary pattern (LBP) for colorectal cancer tissue identification by using support vector machine (SVM) and decision fusion of feature selection. The average experimental results achieve high identification rate which is significantly superior to the existing known methods. In summary, the proposed method based on machine learning outperforms the techniques described in the literatures and achieve high classification accuracy rate at 93.17% for eight classes and 96.02% for ten classes which demonstrate promising applications for cancer tissue classification of histological images.

原文English
出版狀態Published - 7月 2019
事件23rd Pacific Asia Conference on Information Systems: Secure ICT Platform for the 4th Industrial Revolution, PACIS 2019 - Xi'an, China
持續時間: 8 7月 201912 7月 2019

Conference

Conference23rd Pacific Asia Conference on Information Systems: Secure ICT Platform for the 4th Industrial Revolution, PACIS 2019
國家/地區China
城市Xi'an
期間8/07/1912/07/19

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