Faster R-CNN based Framework For Bone Fracture Detection in Telesurgery Systems in Healthcare 4.0

This paper introduces an ideal structure for the detection and treatment of bone fractures utilizing advanced deep learning and telesurgery techniques. The approach begins with the collection of medical imaging data from the X-Ray Layer, which is subsequently divided into training, validation, and testing datasets. The proposed model, ResNet50, is trained on training dataset and validated with the validation dataset to ensure robust performance. Once the model is trained, it accurately detects bone fractures in X-ray images and identifies the region of the fracture using an array of four points representing bounding box defined by four corners. Upon detecting a fracture, telesurgery is employed to treat the patient through a remote arm controlled by a doctor, providing minimally invasive procedures that reduce surgical risks. In case of no fracture, the patient is advised on precautionary measures and follow-up care. This comprehensive framework demonstrates a method for improving the diagnosis and treatment of bone fractures, offering efficient, precise, error-free, and patient-centric solutions. The combination of deep learning with telesurgery shows advancement in medical technology, potentially reducing need for in-person consultations also improving specialized care. Future advancement can include model improvement by using more diverse datasets, integrating additional imaging modalities such as CT and MRI, and expanding real-time capabilities in emergency situations.