# Research on X-ray Diagnosis Model of Musculoskeletal Diseases Based on Deep Learning
## 5 Conclusions and Limitations  
In this study, our primary focus centered on the application of deep learning methodologies in the diagnosis of musculoskeletal (MSK) diseases. Leveraging the MURA dataset, we constructed a hybrid neural network architecture to assess the model's performance, subsequently analyzing the experimental outcomes. Our findings underscore the considerable promise of deep learning methodologies augmented by human involvement, showcasing their potential applicability across diverse practical domains. Examples include but are not limited to spam filtering, misinformation detection on public platforms, and industrial anomaly detection, where the integration of deep learning models with human inputs significantly enhances the data accuracy and efficacy.  
Within our experimental framework, the HRD hybrid model exhibited prediction accuracies ranging between 75% and 90%. Notably, in real-world scenarios, the model can be easily tailored to accommodate various levels of diagnostic accuracy within the Human Block, thereby facilitating medical diagnosis for novice and general practitioners alike, boasting a simplicity of deployment and operational efficiency. Moreover, comparative analysis revealed that the hybrid model outperformed its single-model counterparts across multiple metrics, demonstrating superior generalization capabilities and classification accuracy.  
Nevertheless, the study findings exhibited less discernible impact among the senior physicians specializing in musculoskeletal disease diagnosis. Future research endeavors could explore the replacement of the ResNet50 and DenseNet121 models within the proposed hybrid framework, potentially yielding a more optimized hybrid model. Furthermore, while the present study categorized X-ray image results into binary classifications (i.e., abnormal or normal), future investigations could delve into finer-grained categorizations of abnormal conditions such as fractures, strains, arthritis, etc.  
Importantly, the study overlooked crucial environmental factors, suggesting avenues for future exploration. Incorporating uncertainty estimation techniques such as Monte Carlo dropout could bolster the methodological robustness, reliability, and practical applicability within clinical settings, particularly in critical medical scenarios.  
While our study validated the feasibility of the HRD hybrid model, its efficacy, robustness, and generalization capabilities in identifying musculoskeletal abnormalities warrant further enhancement. Through iterative refinement and adjustment of the model, we aspire to elevate the hybrid model to a gold standard in lesion detection, potentially alleviating diagnostic burdens associated with MSK conditions and facilitating expedited and accurate patient treatment.  
Conceptualization, W.K.; Methodology, Y.S.; Writing--original draft, G.D. and S.Z.; Writing--review & editing, G.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Shaanxi Innovation Capability Support Program (General project of Soft Science Research Program) (grant number 2022 KRM188), the Key R&D Program of Shaanxi Province (General project) (grant number 2023-YBSF-505), and the Doctoral Dissertation Innovation Fund of Xi'an University of Technology (grant number 310-25072119).  
Not applicable.  
Data Availability Statement: Publicly available datasets were analyzed in this study. This data can be found here: [[http://stanfordmlgroup.github.io/competitions/mura](http://stanfordmlgroup.github.io/competitions/mura)]([http://stanfordmlgroup.github.io/competitions/mura](http://stanfordmlgroup.github.io/competitions/mura)) (accessed on 17 April 2024).