A Comprehensive Evaluation with Advanced Error Metrics on Wrist Fracture Detection

Wrist fractures remain one of the biggest dilemmas in orthopedics, and immediate precise diagnosis will directly call for effective treatment planning as well as enhanced outcomes in the patient. Traditional diagnostic approaches have been based on interpretations via bare hand checking of X-ray images, which consequently gives way to variability and reduced accuracy in diagnosis. This paper presents an advanced deep learning framework for significantly improving the accuracy in wrist fracture detection with better error metrics and optimization strategies. On that basis, the given model utilizes the potential of the GRAZPEDWRI-DX dataset; the architecture incorporates a Convolutional Neural Network architecture augmented with Efficient Channel Attention mechanisms to boost the feature extraction process and enhance the precision for fracture localization accuracy. The framework combines more traditional evaluation metrics like precision, recall, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{F 1}$</tex>-score with the detailed error analysis, utilizing Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{R}^{\mathbf{2}}$</tex> score, and Median Squared Error (MdSE). Such an allround evaluation framework conveys more about the robustness of models as well as their predictive performance in terms of the error distribution. Experimentally, the good improvement in the detection of wrist fractures shows that prediction errors are reduced and the overall accuracy of the method is enhanced when compared with other state-of-the-art methods. Therefore, the use of sophisticated metrics of error is an important contribution, proving the crucial role, they will play for more accurate deep learning models of medical imaging tasks, hence enabling more clinically relevant predictions. This work provided a foundation basis for more accurate AI-driven diagnostic applications which potentially shall revolutionize the scope of orthopedic imaging, since the accuracy and reliability of the fracture detection models in clinical settings are improved.