Accurate bladder cancer diagnosis using ensemble deep leaning

• Published in: Scientific reports Vol. 15; no. 1; pp. 12880 - 24
• Main Authors: El-Atier, Rana A., Saraya, M. S., Saleh, Ahmed I., Rabie, Asmaa H.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 15.04.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166; 692/699; 692/700; Algorithms; Artificial intelligence; Biopsy; Bladder cancer; Cancer; Clinical trials; Deep Learning; Diagnosis; Ensemble classification; Humanities and Social Sciences; Humans; multidisciplinary; Neural networks; Neural Networks, Computer; Science; Science (multidisciplinary); Urinary Bladder Neoplasms - diagnosis; Urinary Bladder Neoplasms - diagnostic imaging; Urinary tract; Voting; Deep learning; Ensemble classification; Diagnosis; Voting; Bladder cancer

There are an estimated 1.3 million cases of cancer globally each year, making it one of the most serious types of urinary tract cancer. The methods used today for diagnosing and monitoring bladder cancer are intrusive, costly, and time-consuming. In clinical practice, invasive biopsy followed by his...