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Artificial Intelligence Techniques for Prostate Cancer Detection through Dual-Channel Tissue Feature Engineering

 Cho-Hee Kim  ;  Subrata Bhattacharjee  ;  Deekshitha Prakash  ;  Suki Kang  ;  Nam-Hoon Cho  ;  Hee-Cheol Kim  ;  Heung-Kook Choi 
 CANCERS, Vol.13(7) : 1524, 2021-03 
Journal Title
Issue Date
artificial intelligence ; binary classification ; dual-channel ; prostate cancer ; prostate cancer detection ; texture analysis ; tissue feature engineering
The optimal diagnostic and treatment strategies for prostate cancer (PCa) are constantly changing. Given the importance of accurate diagnosis, texture analysis of stained prostate tissues is important for automatic PCa detection. We used artificial intelligence (AI) techniques to classify dual-channel tissue features extracted from Hematoxylin and Eosin (H&E) tissue images, respectively. Tissue feature engineering was performed to extract first-order statistic (FOS)-based textural features from each stained channel, and cancer classification between benign and malignant was carried out based on important features. Recursive feature elimination (RFE) and one-way analysis of variance (ANOVA) methods were used to identify significant features, which provided the best five features out of the extracted six features. The AI techniques used in this study for binary classification (benign vs. malignant and low-grade vs. high-grade) were support vector machine (SVM), logistic regression (LR), bagging tree, boosting tree, and dual-channel bidirectional long short-term memory (DC-BiLSTM) network. Further, a comparative analysis was carried out between the AI algorithms. Two different datasets were used for PCa classification. Out of these, the first dataset (private) was used for training and testing the AI models and the second dataset (public) was used only for testing to evaluate model performance. The automatic AI classification system performed well and showed satisfactory results according to the hypothesis of this study.
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1. College of Medicine (의과대학) > Dept. of Pathology (병리학교실) > 1. Journal Papers
Yonsei Authors
Kang, Suki(강숙희) ORCID logo https://orcid.org/0000-0002-9957-3479
Cho, Nam Hoon(조남훈) ORCID logo https://orcid.org/0000-0002-0045-6441
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