A Comparative Study of Certain Convolutional Neural Network Architectures for X-ray Image Analysis in Bone Fracture Detection and Identification

Abstract

This research systematically evaluates the performance of diverse Convolutional Neural Network (CNN) architectures in enhancing the accuracy of bone fracture detection in medical imaging. The study aims to understand the intricate nuances exhibited by CNNs when analyzing X-ray images, high-lighting the significance of a robust framework with high sensitivity and specificity. To address the issue of imbalanced datasets, a carefully preprocessed, normalized, and augmented dataset from multiple medical institutions is utilized. The implementation of CNN architectures, such as ResNet, VGGNet, and InceptionNet, involves meticulous configuration and hyperparameter tuning to optimize feature extraction in this complex problem domain. Through extensive experimentation and thorough examination of metrics including accuracy, F1 score, sensitivity, and specificity, the efficacy of each architecture in identifying fractures and distinguishing them from benign anomalies in X-ray images is uncovered. The analysis provides a comprehensive understanding of the strengths and limitations of each architecture, enabling well-informed decisions regarding their suitability in clinical settings. This research represents a significant advancement in the field of bone fracture detection in medical imaging, offering valuable insights into the transformative potential of CNN architectures in improving diagnostic accuracy and informing clinical decision-making.