Leveraging Cutting-Edge Deep Learning Techniques for Superior Prostate Cancer Detection

Authors: Reddy, Karedla Chandana, Shreza, Cheripally, Akhil, Lakkars, Zaitoon, Ruqsar, Kumar, Kottu Santosh, Rout, Saroja Kumar

Source: 2024 6th International Conference on Computational Intelligence and Networks (CINE) Computational Intelligence and Networks (CINE), 2024 6th International Conference on. :1-6 Dec, 2024

Relation: 2024 6th International Conference on Computational Intelligence and Networks (CINE)

Affinity-Based Capturing, Release, and Glycoprofiling of PSA Cancer Biomarker Using Miniaturized Micropillar-Based Platform

Authors: Usman, A., Asghar Eftekhar, A., Adibi, A.

Source: IEEE Sensors Journal IEEE Sensors J. Sensors Journal, IEEE. 24(11):17395-17402 Jun, 2024

Investigating feature extraction by SIFT methods for prostate cancer early detection

Authors: Shadan Mohammed Jihad, Ali A. Alsaud, Firas H. Almukhtar, Shahab Kareem, Raghad Zuhair Yousif

Source: Egyptian Informatics Journal, Vol 29, Iss , Pp 100607- (2025)

Subject Terms: Scale-Invariant Feature Transform (SIFT), Prostate Cancer Detection, MRI Image Analysis, Feature Extraction Techniques, Computer Vision in Medical Diagnostics, Electronic computers. Computer science, QA75.5-76.95

File Description: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S1110866524001701; https://doaj.org/toc/1110-8665

Access URL: https://doaj.org/article/0fc469139ea94b4f8632d7182deca5a0

Hydra: Cancer Detection Leveraging Multiple Heads and Heterogeneous Datasets

Authors: Cuccu, Giuseppe, Jobin, Johan, Clement, Julien, Bhardwaj, Akansha, Reischauer, Carolin, Thony, Harriet, Cudre-Mauroux, Philippe

Source: 2020 IEEE International Conference on Big Data (Big Data) Big Data (Big Data), 2020 IEEE International Conference on. :4842-4849 Dec, 2020

Relation: 2020 IEEE International Conference on Big Data (Big Data)

ProstAtlasDiff: Prostate cancer detection on MRI using Diffusion Probabilistic Models guided by population spatial cancer atlases

Authors: Li, Cynthia Xinran ^{a, ⁎}, Bhattacharya, Indrani ^b, Vesal, Sulaiman ^c, Ghanouni, Pejman ^b, Jahanandish, Hassan ^c, Fan, Richard E. ^c, Sonn, Geoffrey A. ^{c, b, 1}, Rusu, Mirabela ^{b, c, d, 1}

Source: In Medical Image Analysis April 2025 101

Association Between Prostate Cancer Detection Rate and Year of Prostate Biopsy

Authors: Young Jun Uhm, Woojin Bang, Jae Hoon Chung, Cheol Young Oh, Hwanik Kim, Jin Seon Cho

Source: Life, Vol 15, Iss 2, p 260 (2025)

Subject Terms: prostate cancer, prostate cancer detection rate, year of prostate biopsy, prostate biopsy, epidemiology, prostate, Science

File Description: electronic resource

Relation: https://www.mdpi.com/2075-1729/15/2/260; https://doaj.org/toc/2075-1729

Access URL: https://doaj.org/article/b0f92a75a8fe468fb5d9465dc3e8cec7

Prostate Cancer Discernment

Authors: Maheswari, M., Meera, S, Sree, R. Sharmikha, Kalpana, R. A.

Source: 2020 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS) Power, Energy, Control and Transmission Systems (ICPECTS), 2020 International Conference on. :1-3 Dec, 2020

Relation: 2020 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)

ProCDet: A New Method for Prostate Cancer Detection Based on MR Images

Authors: Qian, Y., Zhang, Z., Wang, B.

Source: IEEE Access Access, IEEE. 9:143495-143505 2021

Variability in prostate cancer detection among radiologists and urologists using MRI fusion biopsy

Authors: Hiten D. Patel, Whitney R. Halgrimson, Sarah E. Sweigert, Steven M. Shea, Thomas M. T. Turk, Marcus L. Quek, Alex Gorbonos, Robert C. Flanigan, Ari Goldberg, Gopal N. Gupta

Source: BJUI Compass, Vol 5, Iss 2, Pp 304-312 (2024)

Subject Terms: magnetic resonance imaging, practice variation, prostate biopsy, prostate cancer, prostate cancer detection, Diseases of the genitourinary system. Urology, RC870-923

File Description: electronic resource

Relation: https://doaj.org/toc/2688-4526

Access URL: https://doaj.org/article/7cf4f6036ad44137a604b9142d331fc5

Improved prostate cancer diagnosis using a modified ResNet50-based deep learning architecture

Authors: Fatma M. Talaat, Shaker El-Sappagh, Khaled Alnowaiser, Esraa Hassan

Source: BMC Medical Informatics and Decision Making, Vol 24, Iss 1, Pp 1-14 (2024)

Subject Terms: Prostate cancer detection, Convolution neural network, ResNet50, Dual optimizer, Computer applications to medicine. Medical informatics, R858-859.7

File Description: electronic resource

Relation: https://doaj.org/toc/1472-6947

Access URL: https://doaj.org/article/2f77caa2b6e24d7fa24084d6624579f6

On Computer-Aided Diagnosis of Prostate Cancer from MRI using Machine Intelligence Techniques

Authors: Abraham, Bejoy, Nair, Madhu S.

Source: 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT) Electrical, Computer and Communication Technologies (ICECCT), 2019 IEEE International Conference on. :1-8 Feb, 2019

Relation: 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT)

Fully convolutional neural networks for prostate cancer detection using multi-parametric magnetic resonance images: an initial investigation

Authors: Wang, Yunzhi, Zheng, Bin, Gao, Dashan, Wang, Jiao

Source: 2018 24th International Conference on Pattern Recognition (ICPR) Pattern Recognition (ICPR), 2018 24th International Conference on. :3814-3819 Aug, 2018

Relation: 2018 24th International Conference on Pattern Recognition (ICPR)

A Deep Learning Approach for Targeted Contrast-Enhanced Ultrasound Based Prostate Cancer Detection

Authors: Feng, Y., Yang, F., Zhou, X., Guo, Y., Tang, F., Ren, F., Guo, J., Ji, S.

Source: IEEE/ACM Transactions on Computational Biology and Bioinformatics IEEE/ACM Trans. Comput. Biol. and Bioinf. Computational Biology and Bioinformatics, IEEE/ACM Transactions on. 16(6):1794-1801 Jan, 2019

A Graph-Based Multi-kernel Feature Weight Learning Framework for Detection and Grading of Prostate Lesions Using Multi-parametric MR Images

Authors: Chen, Weifu, Chiu, Bernard, Gibson, Eli, Bastian-Jordan, Matthew, Cool, Derek, Kassam, Zahra, Liang, Huagen, Ward, Aaron, Shen, Qi, Feng, Guocan

Source: 2017 4th IAPR Asian Conference on Pattern Recognition (ACPR) ACPR Pattern Recognition (ACPR), 2017 4th IAPR Asian Conference on. :658-663 Nov, 2017

Relation: 2017 4th IAPR Asian Conference on Pattern Recognition (ACPR)

Advances in Photoacoustic Endoscopic Imaging Technology for Prostate Cancer Detection

Authors: Ningning Wei, Huiting Chen, Bin Li, Xiaojun Dong, Bo Wang

Source: Photonics, Vol 11, Iss 9, p 872 (2024)

Subject Terms: photoacoustic endoscopic imaging, prostate cancer detection, image reconstruction algorithms, Applied optics. Photonics, TA1501-1820

File Description: electronic resource

Relation: https://www.mdpi.com/2304-6732/11/9/872; https://doaj.org/toc/2304-6732

Access URL: https://doaj.org/article/f1035054cd994df49eff6274b53abd23

Targeted Prostate Biopsy: How, When, and Why? A Systematic Review

Authors: Giacomo Rebez, Maria Barbiero, Franco Alchiede Simonato, Francesco Claps, Salvatore Siracusano, Rosa Giaimo, Gabriele Tulone, Fabio Vianello, Alchiede Simonato, Nicola Pavan

Source: Diagnostics, Vol 14, Iss 17, p 1864 (2024)

Subject Terms: targeted prostate biopsy, multiparametric MRI, transrectal ultrasound, diagnostic accuracy, prostate cancer detection, Medicine (General), R5-920

File Description: electronic resource

Relation: https://www.mdpi.com/2075-4418/14/17/1864; https://doaj.org/toc/2075-4418

Access URL: https://doaj.org/article/d4f135ae95574849900917868f8e51ca

An Automated Prostate-cancer Prediction System (APPS) Based on Advanced DFO-ConGA2L Model using MRI Imaging Technique

Authors: Sathesh Abraham Leo E, Nattar Kannan K

Source: Iraqi Journal for Computer Science and Mathematics, Vol 5, Iss 1 (2024)

Subject Terms: Prostate Cancer Detection, Artificial Intelligence (AI), Deep Learning, Magnetic Resonance Image (MRI), Automated Prostate-cancer Prediction System (APPS), Coherence Diffusion Filtering (CDFilter), Dragon Fly Optimized Feature Selection (DFO-FS), and Classification, Electronic computers. Computer science, QA75.5-76.95

File Description: electronic resource

Relation: https://journal.esj.edu.iq/index.php/IJCM/article/view/1400; https://doaj.org/toc/2958-0544; https://doaj.org/toc/2788-7421

Access URL: https://doaj.org/article/cb7d65fe2ab7490ba213a77f1ec74dcf

Digital PI-RADS: Smartphone Sketches for Instant Knowledge Acquisition in Prostate Cancer Detection

Authors: Prange, Alexander, Sonntag, Daniel

Source: 2016 IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS) Computer-Based Medical Systems (CBMS), 2016 IEEE 29th International Symposium on. :13-18 Jun, 2016

Relation: 2016 IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS)

Comparative analysis of minimally invasive methods of treatment of localized prostate cancer

Authors: D. V. Chinenov, E. V. Shpot, Ya. N. Chernov, Z. K. Tsukkiev, A. Yu. Votyakov, A. A. Kurbanov, H. M. Ismailov, Yu. V. Lerner, L. M. Rapoport

Source: Андрология и генитальная хирургия, Vol 23, Iss 2, Pp 34-46 (2022)

Subject Terms: brachytherapy, cryoablation, high-intensity focused ultrasound therapy, prostate cancer detection, Surgery, RD1-811, Diseases of the genitourinary system. Urology, RC870-923

File Description: electronic resource

Relation: https://agx.abvpress.ru/jour/article/view/563; https://doaj.org/toc/2070-9781

Access URL: https://doaj.org/article/8f5c6117e5a647e7aade2eda98cf664c

Nonlinear filtering for enhancing prostate MR images via alpha-trimmed Mean Separation

Authors: Yicong Zhou, Panetta, Karen, Agaian, Sos

Source: 2010 IEEE International Conference on Systems, Man and Cybernetics Systems Man and Cybernetics (SMC), 2010 IEEE International Conference on. :3698-3701 Oct, 2010

Relation: 2010 IEEE International Conference on Systems, Man and Cybernetics - SMC