Detection and localization of wrist fractures in x-ray imagery using deep learning teaching

Hospitals often refer patients with wrist fractures, particularly to their emergency rooms. To accurately diagnose these illnesses and choose the appropriate course of therapy, doctors must evaluate images from various medical equipment, medical data, and a physical assessment of the patient. This project attempts to use deep learning on these images to identify wrist X-ray fractures and help physicians diagnose them, particularly in emergency departments. This study employs a dataset comprising both fractured and regular wrists to assess the extent to which the Recurrent Neural Network with 22 Convolutional Neural Network layers (RN22-CNNs) transfers knowledge for fracture identification and classification. We evaluate the diagnostic accuracy of the RN-21CNN model against four popular transfer learning models, namely Visual Geometry Group (Vgg16), ResNet-50, Inception V3, and Vgg19. We used the model on a dataset of 1644 X-rays collected from the Kaggle repository. Next, we trained, verified, and tested the adapted (RN22-CNNs) model. The proposed model had an accuracy of 96.61%. The proposed Computer-Aided Diagnosis System (CADS) will save medical practitioners' burden by accurately identifying fractures.