A region-of-interest embedded graph neural architecture for gallbladder cancer detection

Gallbladder cancer (GBC) is a reasonably competitive disorder, accounting for almost 50% of biliary tract cancers. Diagnosing GBC is challenging because of its asymptomatic nature in early stages and the similarity in imaging capabilities between benign and malignant gallbladder lesions. Ultrasound imaging, a typically used diagnostic tool, often struggles with issues that include low image quality, noise interference, sensor misalignment, shadows, and misleading textures, in addition to complicating reliable diagnosis. The objective of this study is to use a Region of Interest (ROI)-embedded Graph Neural Network Architecture (RGBNet) to create an advanced artificial intelligence model for the early identification of gallbladder cancer (GBC). Data collection and image pre-processing are the first steps in the procedure, followed by the development of an ROI mask. After that, features are taken out of the ROI and utilized to create graphs, which are fed into RGBNet for accurate GBC detection. RGBNet's use of ROI improves abnormality identification by precisely extracting tumor regions while reducing interference from unimportant regions. In terms of accuracy (93.09%), precision (90.03%), recall (89.77%), specificity (94.73%), and F1-score (91.15%), RGBNet (with ROI) performs better than state-of-the-art models such as MobileNetv3, VGG16, ResNet50, InceptionV3, EfficientNet-B7, RetinaNet, and DenseNet-264 (with only 10 million parameters). Visualization techniques such as Grad-CAM, Guided Grad-CAM, and Guided Backpropagation are used to explain the model's decisions, which help highlight the important regions of the input image that influenced the model's predictions. This approach holds promise for advancing GBC diagnosis through precise, interpretable, and efficient AI methods. • The RGBNet with ROI embedding outperformed baseline models for the classification of complex medical images. • Statistically, the improvements made by the model are significant, with p-values less than 0.05. • The model performs well across various cancer detection datasets, showing its suitability for different medical scenarios. • Grad-CAM and Guided Grad-CAM visualizations improve interpretability, aiding clinical decision-making. • ROI embedding enhances feature extraction, enabling accurate identification of important regions in complex cases.