Colorectal Cancer Diagnostic Algorithm Based on Sub-Patch Weight Color Histogram in Combination of Improved Least Squares Support Vector Machine for Pathological Image

In order to improve the diagnostic accuracy of colon cancer, a novel classification algorithm based on sub-patch weight color histogram and improved SVM is proposed, which has good approximation ability for complex pathological image. Our proposed algorithm combines wavelet kernel SVM with color his...

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Vydáno v:	Journal of medical systems Ročník 43; číslo 9; s. 306 - 9
Hlavní autoři:	Yang, Kai, Zhou, Bi, Yi, Fei, Chen, Yan, Chen, Yingsheng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.09.2019 Springer Nature B.V
Témata:	Algorithms Cancer Classification Colon Colon cancer Color Colorectal cancer Colorectal carcinoma Colorectal Neoplasms - diagnosis Colorectal Neoplasms - pathology Cytodiagnosis - methods Diagnostic systems Distributed Analytics and Deep Learning in Health Care Feature extraction Health Informatics Health Sciences Histograms Humans Image & Signal Processing Image classification Image Processing, Computer-Assisted - methods Kernels Least squares Magnetic Resonance Imaging - methods Medical diagnosis Medical imaging Medicine Medicine & Public Health Morlet wavelet Pattern Recognition, Automated - methods Statistics for Life Sciences Support Vector Machine Support vector machines Weight Pathological image Colon cancer Morlet wavelet Diagnostic Color histogram Support vector machine RelicfF strategy Sub-patch weight
ISSN:	0148-5598, 1573-689X, 1573-689X
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Abstract	In order to improve the diagnostic accuracy of colon cancer, a novel classification algorithm based on sub-patch weight color histogram and improved SVM is proposed, which has good approximation ability for complex pathological image. Our proposed algorithm combines wavelet kernel SVM with color histogram to classify pathological image. Firstly, the pathological image is divided into non-overlapping sub-patches, and the features of sub-patch histogram are extracted. Then, the global and local features are fused by the sub-patch weighting algorithm. Then, the RelicfF based forward selection algorithm is used to integrate color features and texture features so as to enhance the characterization capabilities of the tumor cell. Finally, Morlet wavelet kernel-based least squares support vector machine method is adopted to enhance the generalization ability of the model for small sample with non-linear and high-dimensional pattern classification problems. Experimental results show that the proposed pathological diagnostic algorithm can gain higher accuracy compared with existing comparison algorithms.
AbstractList	In order to improve the diagnostic accuracy of colon cancer, a novel classification algorithm based on sub-patch weight color histogram and improved SVM is proposed, which has good approximation ability for complex pathological image. Our proposed algorithm combines wavelet kernel SVM with color histogram to classify pathological image. Firstly, the pathological image is divided into non-overlapping sub-patches, and the features of sub-patch histogram are extracted. Then, the global and local features are fused by the sub-patch weighting algorithm. Then, the RelicfF based forward selection algorithm is used to integrate color features and texture features so as to enhance the characterization capabilities of the tumor cell. Finally, Morlet wavelet kernel-based least squares support vector machine method is adopted to enhance the generalization ability of the model for small sample with non-linear and high-dimensional pattern classification problems. Experimental results show that the proposed pathological diagnostic algorithm can gain higher accuracy compared with existing comparison algorithms.In order to improve the diagnostic accuracy of colon cancer, a novel classification algorithm based on sub-patch weight color histogram and improved SVM is proposed, which has good approximation ability for complex pathological image. Our proposed algorithm combines wavelet kernel SVM with color histogram to classify pathological image. Firstly, the pathological image is divided into non-overlapping sub-patches, and the features of sub-patch histogram are extracted. Then, the global and local features are fused by the sub-patch weighting algorithm. Then, the RelicfF based forward selection algorithm is used to integrate color features and texture features so as to enhance the characterization capabilities of the tumor cell. Finally, Morlet wavelet kernel-based least squares support vector machine method is adopted to enhance the generalization ability of the model for small sample with non-linear and high-dimensional pattern classification problems. Experimental results show that the proposed pathological diagnostic algorithm can gain higher accuracy compared with existing comparison algorithms. In order to improve the diagnostic accuracy of colon cancer, a novel classification algorithm based on sub-patch weight color histogram and improved SVM is proposed, which has good approximation ability for complex pathological image. Our proposed algorithm combines wavelet kernel SVM with color histogram to classify pathological image. Firstly, the pathological image is divided into non-overlapping sub-patches, and the features of sub-patch histogram are extracted. Then, the global and local features are fused by the sub-patch weighting algorithm. Then, the RelicfF based forward selection algorithm is used to integrate color features and texture features so as to enhance the characterization capabilities of the tumor cell. Finally, Morlet wavelet kernel-based least squares support vector machine method is adopted to enhance the generalization ability of the model for small sample with non-linear and high-dimensional pattern classification problems. Experimental results show that the proposed pathological diagnostic algorithm can gain higher accuracy compared with existing comparison algorithms.
ArticleNumber	306
Author	Chen, Yingsheng Yi, Fei Chen, Yan Yang, Kai Zhou, Bi
Author_xml	– sequence: 1 givenname: Kai surname: Yang fullname: Yang, Kai organization: Department of Radiological Intervention, Shanghai Sixth People’s Hospital East Campus Affiliated to Shanghai University of Medicine & Health Science, Shanghai University of Traditional Chinese Medicine – sequence: 2 givenname: Bi surname: Zhou fullname: Zhou, Bi organization: Department of Radiological Intervention, Shanghai Sixth People’s Hospital East Campus Affiliated to Shanghai University of Medicine & Health Science – sequence: 3 givenname: Fei surname: Yi fullname: Yi, Fei organization: Department of Radiological Intervention, Shanghai Sixth People’s Hospital East Campus Affiliated to Shanghai University of Medicine & Health Science – sequence: 4 givenname: Yan surname: Chen fullname: Chen, Yan organization: Department of Radiological Intervention, Shanghai Sixth People’s Hospital East Campus Affiliated to Shanghai University of Medicine & Health Science – sequence: 5 givenname: Yingsheng surname: Chen fullname: Chen, Yingsheng email: chengyingsheng@hotmail.com organization: Department of Radiological Intervention, Shanghai Sixth People’s Hospital East Campus Affiliated to Shanghai University of Medicine & Health Science, Shanghai University of Traditional Chinese Medicine
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CitedBy_id	crossref_primary_10_1038_s41598_021_93746_z crossref_primary_10_1016_j_measurement_2023_114059 crossref_primary_10_1007_s11831_024_10219_y crossref_primary_10_3389_fgene_2022_842127 crossref_primary_10_1108_DTA_06_2024_0647 crossref_primary_10_1016_j_bbe_2021_09_003 crossref_primary_10_1016_j_optlaseng_2022_107225 crossref_primary_10_3390_math11244937 crossref_primary_10_3748_wjg_v27_i18_2122
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Keywords	Pathological image Colon cancer Morlet wavelet Diagnostic Color histogram Support vector machine RelicfF strategy Sub-patch weight
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SubjectTerms	Algorithms Cancer Classification Colon Colon cancer Color Colorectal cancer Colorectal carcinoma Colorectal Neoplasms - diagnosis Colorectal Neoplasms - pathology Cytodiagnosis - methods Diagnostic systems Distributed Analytics and Deep Learning in Health Care Feature extraction Health Informatics Health Sciences Histograms Humans Image & Signal Processing Image classification Image Processing, Computer-Assisted - methods Kernels Least squares Magnetic Resonance Imaging - methods Medical diagnosis Medical imaging Medicine Medicine & Public Health Morlet wavelet Pattern Recognition, Automated - methods Statistics for Life Sciences Support Vector Machine Support vector machines Weight
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Title	Colorectal Cancer Diagnostic Algorithm Based on Sub-Patch Weight Color Histogram in Combination of Improved Least Squares Support Vector Machine for Pathological Image
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