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

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). © 2017 universiti putra malaysia press.