Improving Diagnostic Accuracy in Radiology with VGG16 Deep Learning Model for Bone Fracture Detection

Identifying bone fractures from medical imaging is a crucial undertaking in diagnostic radiology, having substantial consequences for patient results and healthcare effectiveness. If fractures are not immediately and precisely recognized, they may result in consequences such as delayed healing, chronic discomfort, affecting bone density and perhaps permanent impairment. This research study investigates the use of the VGG16 transfer learning model to automatically diagnose bone fractures from X-ray pictures. The goal is to improve the accuracy and efficiency of diagnosing fractures. The study carefully divides the 'Bone Fracture Multi-Region X-ray Data' dataset, consisting of 10,578 annotated pictures classified as either Fractured or Not Fractured, into three batches: training (9,243 images), validation (829 photos), and test (506 images). The VGG16 model underwent fine-tuning on the training set using data augmentation to enhance its ability to handle fluctuations in imaging circumstances. The performance evaluation included measuring accuracy and loss metrics throughout the training and validation stages. The final assessment on the test set resulted in an amazing overall accuracy of 98.22%. The efficacy of the model was further confirmed by analyzing a confusion matrix, which resulted in good Precision, Recall, and F1 Scores, emphasizing its dependability in detecting fractures. The findings indicate that the VGG16 model may greatly enhance the diagnosis procedure in radiology, decreasing the probability of human mistakes and enhancing patient results. This study emphasizes the potential of deep learning in the processing of biomedical images, which may lead to the development of more sophisticated and automated diagnostic tools for use in clinical practice.