BONE FRACTURE DETECTION

Bone fractures represent a significant and common clinical challenge, with diagnostic accuracy being paramount for effective patient treatment and recovery. While radiographic imaging is the standard diagnostic modality, the manual interpretation process is susceptible to human error, particularly in identifying subtle or hairline fractures. This can lead to missed diagnoses and adverse patient outcomes. In response, deep learning, particularly Convolutional Neural Networks (CNNs), has emerged as a powerful tool for automating medical image analysis. This study presents a rigorous comparative analysis of three distinct CNN-based architectural paradigms for the automated detection of bone fractures from X-ray images. The objective was to evaluate the efficacy of a foundational CNN model, a deep transfer learning model (VGG16), and a region-based object detection model (R-CNN) on a curated dataset of 221 radiographic images. The models were trained and evaluated on their ability to classify images as either “fractured” or “normal.” The experimental results demonstrate a clear performance hierarchy, with the R-CNN model achieving a superior accuracy of 86.32%, significantly outperforming both the VGG16 model (65.8%) and the baseline CNN (55.69%). This principal finding underscores the critical importance of architectural design in medical imaging tasks. The superior performance of the R-CNN framework suggests that for pathologies like bone fractures, which are often localized, two-stage object detection architectures that explicitly identify regions of interest before classification are more effective than global image classification approaches. This research contributes empirical evidence to guide the development of more accurate and reliable automated diagnostic systems, highlighting the potential of region-based models to serve as powerful assistive tools for radiologists.