Hybrid Optimization Enabled Deep-Learning for Prostate Cancer Detection
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11220-024-00495-0.pdf
Reference32 articles.
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2. Yoo, S., Gujrathi, I., Haider, M. A., & Khalvati, F. (2019). Prostate cancer detection using deep convolutional neural networks. Scientific reports, 9(1), 1–10.
3. Heidenreich, A., Bastian, P. J., Bellmunt, J., Bolla, M., Joniau, S., van der Kwast, T., Mason, M., Matveev, V., Wiegel, T., Zattoni, F., & Mottet, N. (2014). EAU guidelines on prostate cancer. Part 1: Screening, diagnosis, and local treatment with curative intent—update 2013. European urology., 65(1), 124–137.
4. De Vente, C., Vos, P., Hosseinzadeh, M., Pluim, J., & Veta, M. (2020). Deep learning regression for prostate cancer detection and grading in bi-parametric MRI. IEEE Transactions on Biomedical Engineering, 68(2), 374–383.
5. Finne, P., Finne, R., Bangma, C., Hugosson, J., Hakama, M., Auvinen, A., & Stenman, U.-H. (2004). Algorithms based on prostate-specific antigen (PSA), free PSA, digital rectal examination and prostate volume reduce false-postitive PSA results in prostate cancer screening. International journal of cancer, 111(2), 310–315.
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