Guest Editor(s)
Prof. Dr. Ayman El-Baz
Email: ayman.elbaz@louisville.edu
Affiliation: Department of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, United States
Homepage:
Research Interests: bioimaging modeling, computer-assisted diagnostic systems, novel image analysis techniques
Prof. Dr. Moumen Elmelegy
Email: moumen.elmelegy@louisville.edu
Affiliation: Department of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, United States
Homepage:
Research Interests: bioimaging modeling, computer-assisted diagnostic systems, novel image analysis techniques
Dr. Asem Ali
Email: asem.ali@louisville.edu
Affiliation: Department of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, United States
Homepage:
Research Interests: bioimaging modeling, computer-assisted diagnostic systems, novel image analysis techniques
Dr. Ali Mahmoud
Email: ali.mahmoud@louisville.edu
Affiliation: Department of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, United States
Homepage:
Research Interests: bioimaging modeling, computer-assisted diagnostic systems, novel image analysis techniques
Summary
Urology is primarily concerned with the diagnosis and treatment of disorders affecting the urinary tract and the male reproductive system. Regardless of whether a condition is complex, such as prostate, bladder, or renal cancer, or relatively simple, such as urinary stones or benign prostatic hyperplasia, early diagnosis plays a significant role in developing an effective treatment plan and improving patient outcomes. Several factors contribute to timely and accurate diagnosis, including the expertise of physicians, advanced medical tools, and, more recently, machine learning–based software, which has advanced rapidly over the past decade.
The aim of this Special Issue is to provide a platform for researchers and clinicians to present the latest advances in machine learning–based diagnostic systems for urological disorders, highlighting novel methodologies and applications that contribute to the early detection, diagnosis, and management of urological disorders.
Potential topics include, but are not limited to:
· Non-invasive computer-aided diagnosis systems for urological disorders
· Prostate cancer diagnosis
· Renal cancer diagnosis
· Bladder cancer diagnosis
· Early detection of acute kidney transplant rejection
· Urological image segmentation
· Deep learning–based classification techniques for urological disorders
Keywords
urology, prostate cancer, bladder cancer, renal cancer, segmentation, diagnosis, machine learning
Login
Register
Submit a Paper
Propose a Special lssue