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AuthorPeyret R.
AuthorBouridane A.
AuthorKhelifi F.
AuthorTahir M.A.
AuthorAl-Maadeed S.
Available date2020-02-05T08:53:35Z
Publication Date2018
Publication NameNeurocomputing
ISSN9252312
URIhttp://dx.doi.org/10.1016/j.neucom.2017.05.010
URIhttp://hdl.handle.net/10576/12744
AbstractThis 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. 2017
SponsorThis work is supported by the Qatar National Research Fund through National Priority Research Program (NPRP) No 6-249-1-053 . The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the Qatar National Research Fund or Qatar University. Appendix A
Languageen
PublisherElsevier B.V.
SubjectAutomatic diagnosis
SubjectColorectal cancer
SubjectHistology
SubjectMultiscale Multispectral Local Binary Pattern
SubjectProstate cancer
SubjectStacked generalization
TitleAutomatic classification of colorectal and prostatic histologic tumor images using multiscale multispectral local binary pattern texture features and stacked generalization
TypeArticle
Pagination83-93
Volume Number275


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