Journal of Medical Systems

, 43:306 | Cite as

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

  • Kai Yang
  • Bi Zhou
  • Fei Yi
  • Yan Chen
  • Yingsheng ChenEmail author
Image & Signal Processing
Part of the following topical collections:
  1. Distributed Analytics and Deep Learning in Health Care


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.


Colon cancer Pathological image Diagnostic Color histogram Morlet wavelet Support vector machine Sub-patch weight RelicfF strategy 



This study is supported by the National Natural Science Foundation of China (No.81571773, 81781771943 81771943), Shanghai municipal health and Family Planning Commission (No.201640191).

Compliance with ethical standards

Conflict of interest

We declare that we have no conflict of interest.

Human or animals participants

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Kai Yang
    • 1
    • 2
  • Bi Zhou
    • 1
  • Fei Yi
    • 1
  • Yan Chen
    • 1
  • Yingsheng Chen
    • 1
    • 2
    Email author
  1. 1.Department of Radiological InterventionShanghai Sixth People’s Hospital East Campus Affiliated to Shanghai University of Medicine & Health ScienceShanghaiChina
  2. 2.Shanghai University of Traditional Chinese MedicineShanghaiChina

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