In a research project at Shahrekord University of Medical Sciences, the accuracy of artificial intelligence data for the rapid and accurate diagnosis of prostate and bladder cancers in men was evaluated. Dr. Fatemeh Taheri, a researcher and faculty member at Shahrekord University of Medical Sciences, stated during a press conference on knowledge translation that bladder cancer, with its increasing incidence and mortality rates, poses a significant health concern.

She added that one of the challenges faced by physicians in diagnosing and grading various cancers, including bladder and prostate cancers, is the manual interpretation of histopathological images, which is both time-consuming and prone to human error. She emphasized that early diagnosis is crucial for better patient outcomes.

Dr. Taheri continued that this research aimed to achieve the automatic detection of bladder cancer through the processing of histopathological images, utilizing a total of 706 images and tissue samples. She noted that highly accurate detection is paramount in image-based tasks, particularly in the analysis of medical images such as histopathological slides.

The faculty member from Shahrekord University of Medical Sciences mentioned that this study enables timely detection through automated interpretation and efficient analysis of large datasets, thereby assisting in early intervention. According to her, the results indicated that the combination of Convolutional Neural Networks (CNN) and Convolutional Block Attention Modules (CBAM) not only enhances diagnostic accuracy but also reduces reliance on manual analyses and errors resulting from pathologist fatigue. This system shows considerable potential for use in clinical settings as a decision-support tool and could pave the way for future research in the area of early detection of bladder cancer.