An Automatic Detection Method for Hematoma Features in Placental Abruption Ultrasound Images Based on Few-Shot Learning

TL;DR

This paper introduces EH-YOLOv11n, a small-sample learning model that automatically detects hematoma features in placental ultrasound images, improving accuracy and robustness for early diagnosis of placental abruption.

Contribution

It presents an enhanced detection model based on few-shot learning with multidimensional optimization techniques, outperforming existing YOLO variants in accuracy and robustness.

Findings

Detection accuracy of 78%, 2.5% higher than YOLOv11n

13.7% improvement over YOLOv8 in accuracy

Superior performance in precision-recall and occlusion scenarios

Abstract

Placental abruption is a severe complication during pregnancy, and its early accurate diagnosis is crucial for ensuring maternal and fetal safety. Traditional ultrasound diagnostic methods heavily rely on physician experience, leading to issues such as subjective bias and diagnostic inconsistencies. This paper proposes an improved model, EH-YOLOv11n (Enhanced Hemorrhage-YOLOv11n), based on small-sample learning, aiming to achieve automatic detection of hematoma features in placental ultrasound images. The model enhances performance through multidimensional optimization: it integrates wavelet convolution and coordinate convolution to strengthen frequency and spatial feature extraction; incorporates a cascaded group attention mechanism to suppress ultrasound artifacts and occlusion interference, thereby improving bounding box localization accuracy. Experimental results demonstrate a detection accuracy of 78%, representing a 2.5% improvement over YOLOv11n and a 13.7% increase over YOLOv8. The model exhibits significant superiority in precision-recall curves, confidence scores, and occlusion scenarios. Combining high accuracy with real-time processing, this model provides a reliable solution for computer-aided diagnosis of placental abruption, holding significant clinical application value.