Computer Applications and Software, Volume. 42, Issue 4, 21(2025)

INTELLIGENT AUXILIARY DIAGNOSIS SYSTEM FOR PROSTATE CANCER BASED ON DEEP LEARNING

Wang Gang1,2, Meng Ning1,2, Zhu Jin3, and Li Chunjie1,2
Author Affiliations
  • 1School of Software, University of Science and Technology of China, Suzhou 215000, Jiangsu, China
  • 2Suzhou Advanced Research Institute, University of Science and Technology of China, Suzhou 215000, Jiangsu, China
  • 3Department of Urology, Second Affiliated Hospital of Soochow University, Suzhou 215000, Jiangsu, China
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    References(12)

    [1] [1] Zhai Z, Zheng Y, Li N, et al. Incidence and disease burden of prostate cancer from 1990 to 2017: Results from the global burden of disease study 2017[J]. Cancer, 2020, 126(9): 1969-1978.

    [3] [3] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB]. arXiv: 1409.1556, 2014.

    [4] [4] Nagpal K, Foote D, Liu Y, et al. Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer[J]. NPJ Digital Medicine, 2019, 2 (1): 1-10.

    [5] [5] Tolkach Y, Dohmgrgen T, Toma M, et al. High-accuracy prostate cancer pathology using deep learning[J]. Nature Machine Intelligence, 2020, 2(7): 411-418.

    [6] [6] Zoph B, Vasudevan V, Shlens J, et al. Learning transferable architectures for scalable image recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2018: 8697-8710.

    [7] [7] Humphrey P A. Gleason grading and prognostic factors in carcinoma of the prostate[J]. Modern Pathology, 2004, 17 (3): 292-306.

    [8] [8] Tan M X, Le Q. Efficientnet: Rethinking model scaling for convolutional neural networks[C]//International Conference on Machine Learning, 2019: 6105-6114.

    [9] [9] Howard A, Sandler M, Chu G, et al. Searching for mobilenetv3[C]//IEEE/CVF International Conference on Computer Vision, 2019: 1314-1324.

    [10] [10] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2018: 7132-7141.

    [11] [11] Girshick R, Donahue J, Darrell T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2014: 580-587.

    [12] [12] Uijlings J R, Sande K E, Gevers T, et al. Selective search for object recognition[J]. International Journal of Computer Vision, 2013, 104(2): 154-171.

    [13] [13] Prostate cANcer graDe assessment (PANDA) challenge[EB/OL]. [2022-01-02]. https://www.kaggle.com/c/prostate-cancer-grade-assessment/overview.

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    Wang Gang, Meng Ning, Zhu Jin, Li Chunjie. INTELLIGENT AUXILIARY DIAGNOSIS SYSTEM FOR PROSTATE CANCER BASED ON DEEP LEARNING[J]. Computer Applications and Software, 2025, 42(4): 21

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    Paper Information

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    Received: Jan. 9, 2022

    Accepted: Aug. 25, 2025

    Published Online: Aug. 25, 2025

    The Author Email:

    DOI:10.3969/j.issn.1000-386x.2025.04.004

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