YOLOv8-AM: YOLOv8 Based on Effective Attention Mechanisms for Pediatric Wrist Fracture Detection

Abstract

Wrist trauma and even fractures occur frequently in daily life, particularly among children who account for a significant proportion of fracture cases. Before performing surgery, surgeons often request patients to undergo X-ray imaging first and prepare for surgery based on the analysis of the radiologists. With the development of neural networks, You Only Look Once (YOLO) series models have been widely used in fracture detection as computer-assisted diagnosis (CAD) tools. Ultralytics presented the latest version of the YOLO models in 2023, which has been employed for detecting fractures across various parts of the body. Attention mechanism is one of the most popular methods to improve the model performance. This work proposes YOLOv8-AM, which incorporates the attention modules into the YOLOv8 architecture. Specifically, we respectively employ four different attention modules, ResBlock with Convolutional Block Attention Module (ResCBAM), Shuffle Attention (SA), Efficient Channel Attention (ECA), and ResBlock with Global Attention Mechanism (ResGAM), to improve the model architecture, and train these models on the GRAZPEDWRI-DX dataset. Experimental results demonstrate that the mean Average Precision at IoU 50 (mAP@50) of one of the variants of YOLOv8-AM model (i.e., YOLOv8+ResCBAM) increased from 63.6% to 65.8%, which achieves the state-of-the-art (SOTA) performance. In addition, the proposed YOLOv8-AM models can detect the single-label category “fracture” with mAP@50 value of 95.7% in pediatric wrist trauma X-ray images. The implementation code for this work is available on GitHub at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/RuiyangJu/Fracture_Detection_Improved_YOLOv8</uri>.

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