Automated Gallbladder Cancer Detection Using CT images and Artificial Intelligence.

Gallbladder cancer (GBC) is among the most fatal malignancies of the biliary tract, commonly detected at advanced stages due to vague clinical manifestations. Conventional radiological assessment is often limited by observer dependency and time requirements, resulting in delayed interventions. This work introduces an artificial intelligence-based diagnostic framework that employs convolutional neural networks (CNNs) to classify gallbladder pathologies from computed tomography (CT) scans. The framework incorporates preprocessing, deep-learning classification, and secure web deployment. Evaluation on a curated dataset demonstrated high accuracy across five classes: cancer, non-cancer, cholecystitis, gallstones, and polyps. Findings highlight the potential of Al-assisted methods to improve early recognition, minimize diagnostic variability, and provide scalable decision support for clinical practice.