# Research on X-ray Diagnosis Model of Musculoskeletal Diseases Based on Deep Learning
## 2 Background Literature  
### Deep learning and related research  
Deep learning models are gradually replacing traditional machine learning (ML) models because they can automatically extract useful features from input data, whereas traditional ML models require manual feature engineering. In the field of biomedical research, the emergence of deep learning has overcome many challenges faced by traditional machine learning methods [13]. One of the most popular deep learning architectures is the convolutional neural network (CNN). CNNs are primarily composed of convolutional layers, pooling layers, and fully connected layers. The convolutional layers are responsible for extracting features from images, pooling layers perform downsampling and dimensionality reduction on the features, and fully connected layers map the features to labels. Compared to traditional neural networks, CNNs excel at automatically extracting abstract feature representations from raw data, especially for image data. Various CNN models and variants have been applied in the biomedical field including tasks such as microscopic image classification [14], X-ray reconstruction [15], breast X-ray detection [16], liver lesion classification [17], brain MRI segmentation [18], and the clustering of plantar pressure images [19]. [PERSON] et al. [20] employed an ensemble model based on CNNs for ankle joint fracture detection. The proposed model was trained on a small dataset, classifying ankle joint radiographs as normal or abnormal. The architecture was implemented on both single-view and three-view integrated models. Some classic CNN models include LeNet-5, AlexNet, ZF-Net, VGGNet, GoogLeNet, ResNet, and DenseNet [21]. The hybrid model proposed in this paper was constructed using parts of ResNet50 and DenseNet121, among others.  
The continuous increase in the depth of traditional neural networks leads to issues such as gradient vanishing, gradient exploding, and model overfitting during the training process [22]. [PERSON] et al. [23], by introducing identity mappings and implementing skip connections, proposed deep residual networks (ResNet) to alleviate the degradation problem in deep networks. ResNet primarily addresses the degradation problem in deep neural networks, where an increase in network depth results in a decline in model performance. Dense convolutional network (DenseNet) focuses on addressing the problem of information loss in deep neural networks. DenseNet tackles this issue through dense connections, achieving notable success in computer vision tasks such as image classification, object detection, and medical image analysis.  
[PERSON] et al. [24] introduced a large dataset for abnormality detection in musculoskeletal X-ray images, where the authors utilized a multi-view 169-layer DenseNet model for image classification. The Cohen's kappa statistic for this model was reported as 0.389. [PERSON] et al. [25] proposed a DL-based abnormality detection for wrist, hand, and ankle joint radiographs. Models like VGG-19 were trained to categorize X-rays into four classes: lateral, fracture, body part, and study view. Except for catagma, all models achieved an accuracy of over 90% for all classes. In this study, we trained and tested our proposed hybrid model using the MURA [24] dataset to enhance the accuracy and efficiency of the model.