Attention-driven deep object detection for improved gallbladder cancer diagnosis from ultrasound images

This study presents an advanced modification of the You Only Look Once version 7 (YOLOv7) model named Gallbladder YOLOv7 (GB-YOLOv7). GB-YOLOv7 integrates a Normalization-based Attention Module (NAM) and a Global Attention Mechanism (GAM) into the backbone and head architecture. Several image preprocessing methods are also employed, including median filtering and Contrast-Limited Adaptive Histogram Equalization (CLAHE). The framework includes three attention mechanism-based models, including Coordinate and Global Attention Mechanism (CordGAM-YOLOv7), Dual Global Attention Mechanism (DualGAM-YOLOv7), and Normalization-based Attention Module YOLOv7 (NAM-YOLOv7), enabling a meticulous comparative analysis with GB-YOLOv7. Results demonstrate its superior performance across all metrics compared to both traditional and newer YOLO versions: achieving a Recall of 91.3% (vs YOLOv8's 78.1% and YOLOv9's 84.8%), a Mean Average Precision of 94.0%, and a Specificity of 96.2% (vs YOLOv11's 90.6%). GB-YOLOv7 also shows significant improvements in Matthews Correlation Coefficient (MCC) (72.7% vs YOLOv7's 67.3%) and F1-score (90.0% vs You Only Look Once version 9 ( YOLOv9)'s 81.4%), while maintaining greater parameter efficiency (24.34M vs YOLOv7's 36.58M), showcasing its potential as a cutting-edge tool for more effective gallbladder cancer detection.