# Deep Learning Approach for Arm Fracture Detection Based on an Improved YOLOv8 Algorithm
## 1 Introduction
More modern uses of CNN methods for medical imaging include the application of IoMT (Internet of Medical Things)-based deep learning architecture for fetal QRS utilizing the ResNet18 and MobileNet pre-trained models, with an accuracy of 83.2% and 88.7%, respectively [22] and the addition of a soft attention block into the DenseNet algorithm utilized to classify SPECT images used for Parkinson's Disease classification, with an overall accuracy of 99.2% [23]. Due to the extensive hardware requirements and inference times related to two-stage CNN algorithms, an emphasis on improving one-stage algorithms such as YOLO has arisen in recent years. The You Only Look Once (YOLO) series is one of the most well-known object detection algorithms with a balance between accuracy, inference speed, system requirements, ease of use, and adaptability, with each iteration building on the previous iterations to improve accuracy and performance [11]. YOLOv8 is a notable iteration in the YOLO series, known for its balance between speed and accuracy in object detection tasks [24]. YOLOv8 offers a variety of model sizes to cater to different computational resources and performance requirements. These models range from YOLOv8n (nano) and YOLOv8s (small) for lightweight applications with limited computational power to YOLOv8m (medium), YOLOv8l (large), and YOLOv8x (extra-large) for scenarios demanding higher accuracy and more detailed detections. Each model size is optimized to provide the best possible performance for its intended use case, making YOLOv8 a versatile choice for diverse computer vision applications, especially in medical imaging that includes bone fracture detection. An improved version of YOLOv2 managed to achieve a mAP value of 75.3% [25] on a dataset of spinal fracture lesions. Because of using a one-stage system, the average detection time per image is 0.027 s, making it extremely useful in emergency rooms. A YOLOv8 with augmented fracture scans obtained an average mAP score of 75.3% [26] from the GRAZPEDWRI-DX dataset [27]. A modified variant of YOLOv7 with an attention-based mechanism (the Squeeze-and-Excitation module) achieved a mAP score of 86.2% on the FracAtlas dataset [28]. An implementation of YOLO-NAS (You Only Look Once- Neural Architecture Search) [29] and Scalable and Efficient Object Detection (EfficientDet) [30] trained on a dataset of 4736 images of hand fractures had an average mAP score of 98.1% [31]. Although they have high accuracy, they lack the precision to detect fine-grained features, lack faster real-time detection on a large medical dataset, and are not flexible and customizable to modify the algorithm, especially with the addition of an attention mechanism. There is a knowledge gap in evaluating the effectiveness of integrating the attention mechanisms into the convolutional neural networks (CNNs) framework. Many studies have concentrated solely on employing CNNs for fracture detection, notably utilizing the existing YOLO series algorithms. Although CNNs generally offer an effective balance between model execution time and accuracy, they often face challenges identifying which features are crucial, potentially overlooking details in more intricate images. This limitation can lead to lower accuracy in detecting smaller objects, such as line fractures. By integrating an attention-based mechanism into CNNs, we can improve detail and object detection capabilities, thereby enhancing the performance of the YOLOv8 model and maintaining its competitiveness among other advanced object detection models in fracture or feature detection. This motivated us to address the knowledge gap by integrating the attention mechanism into the YOLOv8 models, thereby improving their feature extraction capabilities. The Hybrid-Attention (HA) mechanism combines channel and spatial attention modules to allow the network to focus on the most relevant features.