Enhancing Gallstone Detection in Ultrasound Imaging Through Advanced Deep Learning Architectures

Gallstone disease is a widespread global health concern where early and accurate diagnosis is critical for preventing severe complications. While abdominal ultrasound remains the primary, non-invasive diagnostic modality due to its safety and accessibility, its efficacy is often compromised by inherent challenges. These include significant operator dependency, the presence of acoustic artifacts like shadows and noise, and the subtle presentation of stones. This research addresses these limitations by developing and evaluating sophisticated deep learning models for the automated detection of gallstones in ultrasound images. We propose novel convolutional neural network (CNN) architectures integrated with transformer-based attention mechanisms and hybrid models, designed to robustly handle the variability and noise in ultrasound data. A comprehensive preprocessing pipeline was implemented to enhance image quality and standardize inputs. Our models were rigorously trained and validated on a multi-source dataset comprising over 1,300 annotated images. The results demonstrate a substantial improvement in diagnostic performance, achieving high levels of sensitivity and specificity by effectively learning discriminative features and mitigating the impact of artifacts. This study confirms the significant potential of artificial intelligence to serve as a reliable decision-support tool in clinical settings, potentially standardizing diagnostic accuracy and improving patient outcomes through earlier and more precise detection.