Histopathological breast-image classification using concatenated R–G–B histogram information

Abdullah-Al Nahid*, Yinan Kong

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


Breast Cancer is a serious threat to women. The identification of breast cancer relies heavily on histopathological image analysis. Among the different breast-cancer image analysis techniques, classifying the images into Benign and Malignant classes, have been an active area of research. The involvement of machine learning for breast-cancer image classification is also an active area of research. Considering the importance of the breast-cancer image classification, this paper has classified a set of histopathological images into Benign and Malignant classes utilizing Neural Network techniques and Random Forest algorithms. As histopathological images suffer intensity variation, in this paper, we have normalized the intensity information by newly proposed intensity normalization techniques, and classify the images using Neural Network techniques and Tree-based classification tools. Investigation shows that the proposed Normalization technique gives the best performance when we use Neural Network techniques but Tree-based algorithms such as the Random Forest algorithm give better performance when we use images without normalization techniques.

Original languageEnglish
Pages (from-to)513-529
Number of pages17
JournalAnnals of Data Science
Issue number3
Publication statusPublished - Sep 2019


  • Breast-cancer
  • Classification
  • Histopathological image
  • Neural Network
  • Random Forest


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