Self-attention-guided residual deep neural network with multi-scale dilated feature extraction for automated gallbladder disease diagnosis in ultrasound imaging.

BACKGROUND AND

OBJECTIVE

Gallbladder diseases present a critical challenge and can cause serious complications, if not diagnosed and treated promptly. Diseases including gallstone, inflammation, and other abnormalities may lead to a number of significant consequences, such as bile duct obstructions, chronic pain, infections, and in severe cases, life-threatening sepsis or gallbladder cancer.

METHODS

This study presents a novel deep learning-based diagnostic model using an attention-guided residual convolutional neural network to classify nine distinct gallbladder diseases, including gallstones, abdomen and retroperitoneal pathology, cholecystitis, membranous and gangrenous cholecystitis, perforation, polyps and cholesterol crystals, adenomyomatosis, carcinoma, and various causes of gallbladder wall thickening. It combines multi-scale feature extraction using dilated convolutions and, attention mechanisms for refined feature selection, and residual connections to preserve spatial information and prevent vanishing gradient issues.

RESULTS

Experimental findings show an accuracy of 99.17%, and a recall of 98.94%. These findings demonstrate the reliability of the model in distinguishing between different gallbladder pathologies. The presented methodology offers a rapid, accurate, and scalable diagnostic tool, to help clinicians identify gallbladder diseases from complex radiological medical images efficiently and with high accuracy.

CONCLUSIONS

The proposed work has the potential to advance patient care and provides a foundation for robust, efficient, and scalable AI-assisted gallbladder disease diagnosis in clinical practice. The source code is publicly available in the GitHub repository2.