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The musculoskeletal system plays a crucial role in our daily lives, and the accurate diagnosis of musculoskeletal issues is essential for providing effective healthcare. However, the classification of musculoskeletal system radiographs is a complex task, requiring both accuracy and efficiency. This study addresses this challenge by introducing and evaluating a pyramid deep feature extraction model for the automatic classification of musculoskeletal system radiographs. The primary goal of this research is to develop a reliable and efficient solution to classify different upper extremity regions in musculoskeletal radiographs. To achieve this goal, we conducted an end-to-end training process using a pre-trained EfficientNet B0 convolutional neural network (CNN) model. This model was trained on a dataset of radiographic images that were divided into patches of various sizes, including 224 × 224, 112 × 112, 56 × 56, and 28 × 28. From the trained CNN model, we extracted a total of 85,000 features. These features were subsequently subjected to selection using the neighborhood component analysis (NCA) feature selection algorithm and then classified using a support vector machine (SVM). The results of our experiments are highly promising. The proposed model successfully classified various upper extremity regions with high accuracy rates: 92.04% for the elbow region, 91.19% for the finger region, 92.11% for the forearm region, 91.34% for the hand region, 91.35% for the humerus region, 89.49% for the shoulder region, and 92.63% for the wrist region. These results demonstrate the effectiveness of our deep feature extraction model as a potential auxiliary tool in the automatic analysis of musculoskeletal system radiographs. By automating the classification of musculoskeletal radiographs, our model has the potential to significantly accelerate clinical diagnostic processes and provide more precise results. This advancement in medical imaging technology can ultimately lead to better healthcare services for patients. However, future studies are crucial to further refine and test the model for practical clinical applications, ensuring that it integrates seamlessly into medical diagnosis and treatment processes, thus improving the overall quality of healthcare services. [ABSTRACT FROM AUTHOR]

The musculoskeletal system plays a crucial role in our daily lives, and the accurate diagnosis of musculoskeletal issues is essential for providing effective healthcare. However, the classification of musculoskeletal system radiographs is a complex task, requiring both accuracy and efficiency. This study addresses this challenge by introducing and evaluating a pyramid deep feature extraction model for the automatic classification of musculoskeletal system radiographs. The primary goal of this research is to develop a reliable and efficient solution to classify different upper extremity regions in musculoskeletal radiographs. To achieve this goal, we conducted an end-to-end training process using a pre-trained EfficientNet B0 convolutional neural network (CNN) model. This model was trained on a dataset of radiographic images that were divided into patches of various sizes, including 224 × 224, 112 × 112, 56 × 56, and 28 × 28. From the trained CNN model, we extracted a total of 85,000 features. These features were subsequently subjected to selection using the neighborhood component analysis (NCA) feature selection algorithm and then classified using a support vector machine (SVM). The results of our experiments are highly promising. The proposed model successfully classified various upper extremity regions with high accuracy rates: 92.04% for the elbow region, 91.19% for the finger region, 92.11% for the forearm region, 91.34% for the hand region, 91.35% for the humerus region, 89.49% for the shoulder region, and 92.63% for the wrist region. These results demonstrate the effectiveness of our deep feature extraction model as a potential auxiliary tool in the automatic analysis of musculoskeletal system radiographs. By automating the classification of musculoskeletal radiographs, our model has the potential to significantly accelerate clinical diagnostic processes and provide more precise results. This advancement in medical imaging technology can ultimately lead to better healthcare services for patients. However, future studies are crucial to further refine and test the model for practical clinical applications, ensuring that it integrates seamlessly into medical diagnosis and treatment processes, thus improving the overall quality of healthcare services.