# Research on X-ray Diagnosis Model of Musculoskeletal Diseases Based on Deep Learning
###### Abstract  
Musculoskeletal diseases affect over 100 million people globally and are a leading cause of severe, prolonged pain, and disability. Recognized as a clinical emergency, prompt and accurate diagnosis of musculoskeletal disorders is crucial, as delayed identification poses the risk of amputation for patients, and in severe cases, can result in life-threatening conditions such as bone cancer. In this paper, a hybrid model HRD (Human-Resnet50-Densenet121) based on deep learning and human participation is proposed to efficiently identify disease features by classifying X-ray images. Feasibility testing of the model was conducted using the MURA dataset, with metrics such as accuracy, recall rate, F1-score, ROC curve, Cohen's kappa, and AUC values employed for evaluation. Experimental results indicate that, in terms of model accuracy, the hybrid model constructed through a combination strategy surpassed the accuracy of any individual model by more than 4%. The model achieved a peak accuracy of 88.81%, a maximum recall rate of 94%, and the highest F1-score value of 87%, all surpassing those of any single model. The hybrid model demonstrates excellent generalization performance and classification accuracy. musculoskeletal diseases; X-ray images; HRD hybrid model; MURA dataset; mixed model strategy +
Footnote †: journal: Medicalintervention is required for extracting features from medical images, leading to relatively low recognition accuracy [5; 6]. Additionally, single models often fail to achieve the desired results in medical image recognition and classification, exhibiting weak adaptability and robustness. For instance, [PERSON] et al. [7] achieved a segmentation of 83% Dice similarity coefficient for three-dimensional CT images of the prostate by using an improved U-Net network. Deep learning, as a data-driven approach, can automatically learn feature representations from a large volume of medical imaging data, thereby enhancing the diagnostic accuracy and efficiency. For instance, [PERSON], [PERSON], and others [8] proposed a deformable Transformer-assisted model for the diagnosis of chest X-ray image diseases, utilizing the extended ResNet50 as a feature extraction network. Multiple experiments were conducted on the ChestX-Ray14 and CheXpert datasets, achieving AUC values of 0.8398 and 0.9061, respectively. [PERSON], [PERSON] et al. [9] proposed a new AI-PLAX, based on two-stage photographs, that addressed the challenge of using photographs for the automated evaluation and examination of the placenta, which was capable of accurate morphological characterization and performed well in clinically meaningful feature analysis tasks. [PERSON] et al. [10] introduced the ConvOS model for the diagnosis of COVID-19, utilizing the IGOS++ algorithm and an improved ConvNeXt network. The ConvOS model achieved an accuracy, recall rate, precision, and F1-score of 93.7%, 92.6%, 96.2%, and 94.4%, respectively. In the field of medical imaging, medical image data present complex characteristics including noise, uncertainty, and class imbalance, which significantly impact physicians during pathological diagnosis [11]. However, the models still face challenges such as limited image granularity and interpretability [12], restricting their application in artificial intelligence-assisted diagnostic systems. Addressing these issues, this paper proposes a machine ensemble strategy by combining multiple models and introducing a participatory deep learning HRD hybrid model. The aim is to enhance model recognition accuracy, adaptability, and robustness, facilitating efficient and accurate assistance for clinicians in diagnosing MSK diseases from X-ray images. To validate the feasibility and accuracy of our research, the MURA dataset was selected for model testing and evaluation. Each image in the MURA dataset has been manually annotated by radiologists. Experimenting with this diverse dataset will enable a comprehensive assessment of the model's performance and generalization ability.