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Ensemble Learning of Tissue Components for Prostate Histopathology Image Grading

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@article{IJASEIT1489,
   author = {Dheeb Albashish and Shahnorbanun Sahran and Azizi Abdullah and Nordashima Abd Shukor and Suria Pauzi},
   title = {Ensemble Learning of Tissue Components for Prostate Histopathology Image Grading},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {6},
   number = {6},
   year = {2016},
   pages = {1134--1140},
   keywords = {Ensemble machine learning; naïve approach; typical CAD system; prostate cancer histological image; tissue components.},
   abstract = {Ensemble learning is an effective machine learning approach to improve the prediction performance by fusing several single classifier models. In computer-aided diagnosis system (CAD), machine learning has become one of the dominant solutions for tissue images diagnosis and grading. One problem in a single classifier model for multi-components of the tissue images combination to construct dense feature vectors is the overfitting. In this paper, an ensemble learning for multi-component tissue images classification approach is proposed. The prostate cancer Hematoxylin and Eosin (H&E) histopathology images from HUKM were used to test the proposed ensemble approach for diagnosing and Gleason grading. The experiments results of several prostate classification tasks, namely, benign vs. Grade 3, benign vs.Grade4, and Grade 3vs.Grade 4 show that the proposed ensemble significantly outperforms the previous typical CAD and the naïve approach that combines the texture features of all tissue component directly in dense feature vectors for a classifier.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1489},
   doi = {10.18517/ijaseit.6.6.1489}
}

EndNote

%A Albashish, Dheeb
%A Sahran, Shahnorbanun
%A Abdullah, Azizi
%A Abd Shukor, Nordashima
%A Pauzi, Suria
%D 2016
%T Ensemble Learning of Tissue Components for Prostate Histopathology Image Grading
%B 2016
%9 Ensemble machine learning; naïve approach; typical CAD system; prostate cancer histological image; tissue components.
%! Ensemble Learning of Tissue Components for Prostate Histopathology Image Grading
%K Ensemble machine learning; naïve approach; typical CAD system; prostate cancer histological image; tissue components.
%X Ensemble learning is an effective machine learning approach to improve the prediction performance by fusing several single classifier models. In computer-aided diagnosis system (CAD), machine learning has become one of the dominant solutions for tissue images diagnosis and grading. One problem in a single classifier model for multi-components of the tissue images combination to construct dense feature vectors is the overfitting. In this paper, an ensemble learning for multi-component tissue images classification approach is proposed. The prostate cancer Hematoxylin and Eosin (H&E) histopathology images from HUKM were used to test the proposed ensemble approach for diagnosing and Gleason grading. The experiments results of several prostate classification tasks, namely, benign vs. Grade 3, benign vs.Grade4, and Grade 3vs.Grade 4 show that the proposed ensemble significantly outperforms the previous typical CAD and the naïve approach that combines the texture features of all tissue component directly in dense feature vectors for a classifier.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1489
%R doi:10.18517/ijaseit.6.6.1489
%J International Journal on Advanced Science, Engineering and Information Technology
%V 6
%N 6
%@ 2088-5334

IEEE

Dheeb Albashish,Shahnorbanun Sahran,Azizi Abdullah,Nordashima Abd Shukor and Suria Pauzi,"Ensemble Learning of Tissue Components for Prostate Histopathology Image Grading," International Journal on Advanced Science, Engineering and Information Technology, vol. 6, no. 6, pp. 1134-1140, 2016. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.6.6.1489.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Albashish, Dheeb
AU  - Sahran, Shahnorbanun
AU  - Abdullah, Azizi
AU  - Abd Shukor, Nordashima
AU  - Pauzi, Suria
PY  - 2016
TI  - Ensemble Learning of Tissue Components for Prostate Histopathology Image Grading
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 6 (2016) No. 6
Y2  - 2016
SP  - 1134
EP  - 1140
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Ensemble machine learning; naïve approach; typical CAD system; prostate cancer histological image; tissue components.
N2  - Ensemble learning is an effective machine learning approach to improve the prediction performance by fusing several single classifier models. In computer-aided diagnosis system (CAD), machine learning has become one of the dominant solutions for tissue images diagnosis and grading. One problem in a single classifier model for multi-components of the tissue images combination to construct dense feature vectors is the overfitting. In this paper, an ensemble learning for multi-component tissue images classification approach is proposed. The prostate cancer Hematoxylin and Eosin (H&E) histopathology images from HUKM were used to test the proposed ensemble approach for diagnosing and Gleason grading. The experiments results of several prostate classification tasks, namely, benign vs. Grade 3, benign vs.Grade4, and Grade 3vs.Grade 4 show that the proposed ensemble significantly outperforms the previous typical CAD and the naïve approach that combines the texture features of all tissue component directly in dense feature vectors for a classifier.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1489
DO  - 10.18517/ijaseit.6.6.1489

RefWorks

RT Journal Article
ID 1489
A1 Albashish, Dheeb
A1 Sahran, Shahnorbanun
A1 Abdullah, Azizi
A1 Abd Shukor, Nordashima
A1 Pauzi, Suria
T1 Ensemble Learning of Tissue Components for Prostate Histopathology Image Grading
JF International Journal on Advanced Science, Engineering and Information Technology
VO 6
IS 6
YR 2016
SP 1134
OP 1140
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Ensemble machine learning; naïve approach; typical CAD system; prostate cancer histological image; tissue components.
AB Ensemble learning is an effective machine learning approach to improve the prediction performance by fusing several single classifier models. In computer-aided diagnosis system (CAD), machine learning has become one of the dominant solutions for tissue images diagnosis and grading. One problem in a single classifier model for multi-components of the tissue images combination to construct dense feature vectors is the overfitting. In this paper, an ensemble learning for multi-component tissue images classification approach is proposed. The prostate cancer Hematoxylin and Eosin (H&E) histopathology images from HUKM were used to test the proposed ensemble approach for diagnosing and Gleason grading. The experiments results of several prostate classification tasks, namely, benign vs. Grade 3, benign vs.Grade4, and Grade 3vs.Grade 4 show that the proposed ensemble significantly outperforms the previous typical CAD and the naïve approach that combines the texture features of all tissue component directly in dense feature vectors for a classifier.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1489
DO  - 10.18517/ijaseit.6.6.1489