Lumen-nuclei ensemble machine learning system for diagnosing prostate cancer in histopathology images

Dheeb Albashish, Shahnorbanun Sahran, Azizi Abdullah, Nordashima Abd Shukor, Suria Hayati Md Pauzi

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

The Gleason grading system assists in evaluating the prognosis of men with prostate cancer. Cancers with a higher score are more aggressive and have a worse prognosis. The pathologists observe the tissue components (e.g. lumen, nuclei) of the histopathological image to grade it. The differentiation between Grade 3 and Grade 4 is the most challenging, and receives the most consideration from scholars. However, since the grading is subjective and time-consuming, a reliable computer-aided prostate cancer diagnosing techniques are in high demand. This study proposed an ensemble computer-added system (CAD) consisting of two single classifiers: a) a specialist, trained specifically for texture features of the lumen and the other for nuclei tissue component; b) a fusion method to aggregate the decision of the single classifiers. Experimental results show promising results that the proposed ensemble system (area under the ROC curve (Az) of 88.9% for Grade 3 versus Grad 4 classification task) impressively outperforms the single classifier of nuclei (Az=87.7) and lumen (Az=86.6).

Original languageEnglish
Pages (from-to)39-48
Number of pages10
JournalPertanika Journal of Science and Technology
Volume25
Issue numberS6
Publication statusPublished - 1 Jun 2017

Fingerprint

histopathology
artificial intelligence
prostatic neoplasms
prognosis
Learning systems
cancer
Prostatic Neoplasms
Classifiers
Neoplasm Grading
Computer Systems
ROC Curve
Tissue
Area Under Curve
computer system
texture
neoplasms
Computer systems
Fusion reactions
Textures
methodology

Keywords

  • Ensemble machine learning
  • Gleason grading system
  • Lumen
  • Nuclei
  • Prostate cancer histological image
  • Tissue components

ASJC Scopus subject areas

  • Computer Science(all)
  • Chemical Engineering(all)
  • Environmental Science(all)
  • Agricultural and Biological Sciences(all)

Cite this

Lumen-nuclei ensemble machine learning system for diagnosing prostate cancer in histopathology images. / Albashish, Dheeb; Sahran, Shahnorbanun; Abdullah, Azizi; Abd Shukor, Nordashima; Md Pauzi, Suria Hayati.

In: Pertanika Journal of Science and Technology, Vol. 25, No. S6, 01.06.2017, p. 39-48.

Research output: Contribution to journalArticle

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