Deep Convolutional Neural Network-Based Model for Precise Grading and Diagnosis of Bladder Cancer from Pathological Images

Bladder cancer is a prevalent malignant tumor in the urinary system, with rising incidence and mortality rates, posing a significant challenge to global public health. Traditional diagnostic methods for bladder cancer rely on the subjective visual inspection of pathologists, which are prone to inter-observer variability and misclassification. With the advancement of artificial intelligence (AI) technologies, deep learning models have demonstrated immense potential in medical image analysis. This study presents a convolutional neural network (CNN)-based deep learning model for precise grading and diagnosis of bladder cancer. The model preprocesses pathological slide images through size normalization, color standardization, and noise removal, followed by feature extraction using the ResNet50 architecture. Sequence data are then processed using a long short-term memory network (LSTM). Additionally, a fusion module (FCS-Block) is integrated to enhance classification performance. Compared with existing methods, the proposed model achieves improved accuracy (85%), sensitivity (84%), and specificity (86%). This research provides a more reliable and effective diagnostic tool for bladder cancer, advancing the automation and standardization of pathological image processing. The model holds significant implications for clinical diagnosis and treatment in the future.