CNN-based High-Precision Model for Accurate Fracture Detection

Abstract

Accurate fracture classification is essential for efficient diagnosis and treatment planning. This paper presents a CNN-based automated fracture classification model and verifies its high accuracy and reliability. The model was verified with a confusion matrix heatmap, which verified its exceptional performance with negligible misclassifications. Fracture Dislocation, Comminuted Fracture, and Pathological Fracture were correctly identified with 100% accuracy, and misclassification errors were insignificant. The model achieved an overall accuracy of 99%, and precision, recall, and F1-scores were always between the range of 0.97 and 1.00. High macro-average and weighted average scores also validate its generalizability. The CNN architecture, consisting of convolutional, pooling, and fully connected layers, efficiently extracts and processes useful features in a computationally efficient way. Training analysis indicated a high convergence trend, with accuracy progressively improved and loss substantially decreased with each epoch. The steep decline of loss in initial stages of training verifies an optimized learning process, which testifies to the reliability of the model. This paper suggests that the proposed CNN model offers a strong and efficient solution for automated fracture detection. Future work will involve dataset expansion, including heterogeneous imaging modalities, and model optimization for clinical deployment.

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