A Real Time Comprehensive Framework for Automated Bone Abnormality Detection using Radiologic Imaging

Detecting fractures and bone tumors from X-ray images is a critical medical diagnosis area, which requires precision and efficiency for better patient care. Automatic detection of fracture and tumor from radiographic images using the DenseNet-121 deep learning architecture is examined here. DenseNet-121 has densely connected convolutional layers and efficient feature propagation that provide a reliable framework for analyzing medical images. The experimental results indicate the model's capability to accurately classify and detect anomalies, offering a potential solution to help radiologists in practice. The approach can have the potential to reduce diagnostic time, improve detection accuracy, and increase the accessibility of healthcare in resource-limited environments.