An Intelligent System for Automated Detection and Identification of Bone Trauma Using Deep Learning

Fractures and bone trauma are serious injuries that are increasing in frequency worldwide. In some cases, these injuries are not easily visible through traditional diagnostic methods such as x-rays, leading to misdiagnosis and inadequate treatment. To address this issue, a Computer-Aided Diagnosis and Recommendation System could be developed, which utilizes various deep learning techniques to accurately detect the severity of the fracture and recommend appropriate exercises, diet plans, and surgeries for recovery. This system would incorporate techniques such as deep learning convolutional neural networks, edge detection, ridge regression, and image smoothing to enhance accuracy and provide more precise recommendations. Each technique would contribute unique features to the system, resulting in better outcomes for patients.