Automatic Abdominal Hernia Mesh Detection Based on YOLOM

Abstract

As a new 3-D ultrasound imaging method, an automated breast ultrasound (ABUS) has been widely used in breast abnormality examinations. Because of its excellent 3D visualization, ABUS is also well suited to the detection of an abdominal wall hernia mesh. Due to the inherent low signal-to-noise ratio of ultrasound imaging and the large amount of data generated during ABUS scanning, mesh detection based on subjective observation is extremely time-consuming and prone to missed detection. Therefore, we proposed a novel abdominal hernia wall mesh detection method based on the you only look once version 3 (YOLOv3) method named the YOLOv3 for mesh (YOLOM) method to detect abdominal wall hernia mesh to speed up the ABUS reading process. To make a YOLOM method with a good detection efficiency, we utilized a lightweight cross stage partial attention network (CSPA-Net) as the backbone and applied a feature enhancement network (FEP-Net) to boost the mesh detection accuracy. An improved loss function with completed intersection-over-union (CIoU) and the Swish activation function were also employed to optimize the proposed YOLOM method. We designed ablation study to verify the validity of the proposed method. The average mesh detection precision reached 98.36%, which was 12.51% and 2.35% higher than that of the YOLOv3 and you only look once version 4 (YOLOv4) methods, respectively. The experimental results and comparisons demonstrated that the proposed YOLOM detector is efficient for abdominal wall hernia mesh detection.

Keywords

• Abdominal wall hernia mesh
• automated 3-D ultrasound
• YOLO
• attention mechanism