Gallstone disease is a prevalent chronic condition impacting individuals
worldwide, posing significant challenges to healthcare systems globally. It ranks
among the most common ailments encountered by individuals seeking emergency care
due to abdominal discomfort. The complexity of gallbladder ultrasound scans arises
from numerous factors, including variations in gallbladder anatomy. In this study, we
propose a healthcare informatics system aimed at identifying and analyzing gallstones.
We conduct a thorough examination of several state-of-the-art object detection
algorithms, including Faster Region-based Convolutional Neural Network (Faster RCNN), Mask Region-based Convolutional Neural Network (Mask R-CNN), and Single
Shot Detector (SSD) Our approach, which combines elements of Mask R-CNN, SSD,
and Faster R-CNN, facilitates the precise detection of gallstones within the gallbladder
by leveraging region-based proposals. We specifically focus on training the Mask RCNN model with various backbone networks. Ultrasound images utilized in our
experiments were sourced from medical professionals, encompassing diverse
demographic characteristics such as gender, age, and urban/rural residence. Our
findings demonstrate that the Mask R-CNN model, with a Resnet-101-FPN backbone
network, excels in gallstone detection, surpassing alternative techniques in object
localization accuracy.
Keywords: Deep learning, Faster R-CNN, Healthcare, Informatics system, Mask R-CNN, Streamlit, SSD.
