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Abstract

Bladder cancer is a leading cause of morbidity and mortality worldwide, with early detection being critical for effective management and improved patient outcomes. Urine cytology is a standard non-invasive screening tool but suffers from low sensitivity, especially for low-grade tumors. Recent advancements in artificial intelligence (AI) and deep learning offer new possibilities to enhance diagnostic accuracy. This paper presents a novel deep learning-enhanced urine cytology framework designed to improve the early detection of bladder cancer. Using convolutional neural networks (CNNs), the system automatically analyzes digitized cytology slides, distinguishing malignant from benign urothelial cells with high precision. This approach aims to address limitations in traditional cytological evaluation, offering a scalable, non-invasive, and highly accurate diagnostic tool suitable for both clinical and remote settings.