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https://ptsldigital.ukm.my/jspui/handle/123456789/578192| Title: | Lumen-nuclei ensemble machine learning system for diagnosing prostate cancer in histopathology images |
| Authors: | Dheeb Albashish Shahnorbanun Sahran (UKM) Azizi Abdullah (UKM) Nordashima Abd Shukor (UKM) Suria Hayati Md Pauzi (UKM) |
| Keywords: | Ensemble machine learning Gleason grading system Lumen Nuclei Prostate cancer histological image Tissue components |
| Issue Date: | Jun-2017 |
| Description: | 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). |
| News Source: | Pertanika Journals |
| ISSN: | 0128-7680 |
| Volume: | 25 |
| Pages: | 39-48 |
| Publisher: | Universiti Putra Malaysia Press |
| Appears in Collections: | Journal Content Pages/ Kandungan Halaman Jurnal |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| ukmvital_113375+Source01+Source010.PDF | 1.3 MB | Adobe PDF |  View/Open |
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