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Article

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

1
Department of Digital Anti-Aging Healthcare, Inje University, Gimhae 50834, Korea
2
Department of Computer Engineering, u-AHRC, Inje University, Gimhae 50834, Korea
3
Department of Pathology, Yonsei University Hospital, Seoul 03722, Korea
*
Authors to whom correspondence should be addressed.
Academic Editors: Keyvan Farahani and Bardia Yousefi
Cancers 2021, 13(7), 1524; https://doi.org/10.3390/cancers13071524
Received: 1 February 2021 / Revised: 9 March 2021 / Accepted: 23 March 2021 / Published: 26 March 2021
(This article belongs to the Special Issue Medical Imaging and Machine Learning​)
Artificial intelligence techniques were used for the detection of prostate cancer through tissue feature engineering. A radiomic method was used to extract the important features or information from histopathology tissue images to perform binary classification (i.e., benign vs. malignant). This method can identify a histological pattern that is invisible to the human eye, which helps researchers to predict and detect prostate cancer. We used different performance metrics to evaluate the results of the classification. In the future, it is expected that a method like radiomic will provide a consistent contribution to analyze histopathology tissue images and differentiate between cancerous and noncancerous tumors.
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. View Full-Text
Keywords: artificial intelligence; tissue feature engineering; dual-channel; prostate cancer; texture analysis; binary classification; prostate cancer detection artificial intelligence; tissue feature engineering; dual-channel; prostate cancer; texture analysis; binary classification; prostate cancer detection
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MDPI and ACS Style

Kim, C.-H.; Bhattacharjee, S.; Prakash, D.; Kang, S.; Cho, N.-H.; Kim, H.-C.; Choi, H.-K. Artificial Intelligence Techniques for Prostate Cancer Detection through Dual-Channel Tissue Feature Engineering. Cancers 2021, 13, 1524. https://doi.org/10.3390/cancers13071524

AMA Style

Kim C-H, Bhattacharjee S, Prakash D, Kang S, Cho N-H, Kim H-C, Choi H-K. Artificial Intelligence Techniques for Prostate Cancer Detection through Dual-Channel Tissue Feature Engineering. Cancers. 2021; 13(7):1524. https://doi.org/10.3390/cancers13071524

Chicago/Turabian Style

Kim, Cho-Hee, Subrata Bhattacharjee, Deekshitha Prakash, Suki Kang, Nam-Hoon Cho, Hee-Cheol Kim, and Heung-Kook Choi. 2021. "Artificial Intelligence Techniques for Prostate Cancer Detection through Dual-Channel Tissue Feature Engineering" Cancers 13, no. 7: 1524. https://doi.org/10.3390/cancers13071524

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