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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2020.12
- 수록면
- 1,486 - 1,495 (10page)
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초록· 키워드
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The analysis of digital microscopy images plays a vital role in computer-aided diagnosis (CAD) and prognosis. The main purpose of this paper is to develop a machine learning technique to predict the histological grades in prostate biopsy. To perform a multiclass classification, an AI-based deep learning algorithm, a multichannel convolutional neural network (MCCNN) was developed by connecting layers with artificial neurons inspired by the human brain system. The histological grades that were used for the analysis are benign, grade 3, grade 4, and grade 5. The proposed approach aims to classify multiple patterns of images extracted from the whole slide image (WSI) of a prostate biopsy based on the Gleason grading system. The Multichannel Convolution Neural Network (MCCNN) model takes three input channels (Red, Green, and Blue) to extract the computational features from each channel and concatenate them for multiclass classification. Stain normalization was carried out for each histological grade to standardize the intensity and contrast level in the image. The proposed model has been trained, validated, and tested with the histopathological images and has achieved an average accuracy of 96.4%, 94.6%, and 95.1%, respectively.
목차
ABSTRACT
1. INTRODUCTION
2. RELATED WORKS
3. MATERIALS AND METHODS
4. RESULTS AND DISCUSSION
5. CONCLUSION
REFERENCE
참고문헌 (20)
참고문헌 신청
M.S. Chung, M. Shim, J.S. Cho, W. Bang, S.I. Kim, S.Y. Cho, et al., “Pathological Characteristics of Prostate Cancer in Men Aged< 50 Years Treated with Radical Prostatectomy: A Multi-centre Study in Korea,” J ournal of Korean Medical Science, Vol. 34, No. 10, pp. 1-10, 2019.
D. Albashish, S. Sahran, A. Abdullah, A. Adam, and M. Alweshah, “A Hierarchical Classifier for Multiclass Prostate Histopathology Image Gleason Grading,” Journal of Information and Communication Technology, Vol. 17, No. 2, pp. 323-346, 2018.
S. Yoo, I. Gujrathi, M.A. Haider, and F. Khalvati, “Prostate Cancer Detection Using Deep Convolutional Neural Networks,” Scientific Reports Vol. 9, No. 1, pp. 1-10, 2019.
S. Chakraborty, S. Aich, A. Kumar, S. Sarkar, J.S. Sim, and H.C. Kim, “Detection of Cancerous Tissue in Histopathological Images Using Dual-channel Residual Convolutional Neural Networks (DCRCNN),” Proceeding of International Conference on Advanced Communication Technology, pp. 197-202, 2020.
A.M. Khan, N. Rajpoot, D. Treanor, and D. Magee, “A Nonlinear Mapping Approach to Stain Normalization in Digital Histopathology Images Using Image-specific Color Deconvolution,” IEEE Transactions on Biomedical Engineering, Vol. 61, No. 6, pp. 1729-1738, 2014.
D. Albashish, S. Sahran, A. Abdullah, A. Adam, and M. Alweshah, “A Hierarchical Classifier for Multiclass Prostate Histopathology Image Gleason Grading,” Journal of Information and Communication Technology, Vol. 17, No. 2, pp. 323-346, 2018.
S. Yoo, I. Gujrathi, M.A. Haider, and F. Khalvati, “Prostate Cancer Detection Using Deep Convolutional Neural Networks,” Scientific Reports Vol. 9, No. 1, pp. 1-10, 2019.
S. Chakraborty, S. Aich, A. Kumar, S. Sarkar, J.S. Sim, and H.C. Kim, “Detection of Cancerous Tissue in Histopathological Images Using Dual-channel Residual Convolutional Neural Networks (DCRCNN),” Proceeding of International Conference on Advanced Communication Technology, pp. 197-202, 2020.
A.M. Khan, N. Rajpoot, D. Treanor, and D. Magee, “A Nonlinear Mapping Approach to Stain Normalization in Digital Histopathology Images Using Image-specific Color Deconvolution,” IEEE Transactions on Biomedical Engineering, Vol. 61, No. 6, pp. 1729-1738, 2014.
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