Automatic classification of colorectal and prostatic histologic tumor images using multiscale multispectral local binary pattern texture features and stacked generalization

NEUROCOMPUTING

卷:275

Article

Peyret, Remy¹  Bouridane, Ahmed¹  Khelifi, Fouad¹  Tahir, Muhammad Atif²  Al-Maadeed, Somaya³ 

[1] Northumbria Univ, Fac Engn & Environm, Dept Comp & Informat Sci, Newcastle Upon Tyne, Tyne & Wear, England

[2] Natl Univ Comp & Emerging Sci NUCES FAST, Dept Comp Sci, Karachi Campus, Karachi, Pakistan

[3] Qatar Univ, Coll Engn, Dept Comp Sci & Engn, Doha, Qatar

关键词: Multiscale Multispectral Local Binary;    Pattern;    Stacked generalization;    Histology;    Colorectal cancer;    Prostate cancer;    Automatic diagnosis;   

DOI  :  10.1016/j.neucom.2017.05.010

来源: Elsevier

【 摘 要 】

This paper proposes a new multispectral multiscale local binary pattern feature extraction method for automatic classification of colorectal and prostatic tumor biopsies samples. A multilevel stacked generalization classification technique is also proposed and the key idea of the paper considers a grade diagnostic problem rather than a simple malignant versus tumorous tissue problem using the concept of multispectral imagery in both the visible and near infrared spectra. To validate the proposed algorithm performances, a comparative study against related works using multispectral imagery is conducted including an evaluation on three different multiclass datasets of multispectral histology images: two representing images of colorectal biopsies - one dataset was acquired in the visible spectrum while the second captures near-infrared spectra. The proposed algorithm achieves an accuracy of 99.6% on the different datasets. The results obtained demonstrate the advantages of infrared wavelengths to capture more efficiently the most discriminative information. The results obtained show that our proposed algorithm outperforms other similar methods. (c) 2017 Published by Elsevier B.V.

【 授权许可】

Free