Modeling Radiologist Decision Making for Paediatric Elbow Fractures by Deep Learning Multiview

Abstract

Abstract: To detect an elbow fracture, patients typically need to take frontal and side views of diagnostic elbow radiographs. For the classification of elbow fracture subtypes, we propose a multiview deep learning technique in this work. Our strategy makes advantage of transfer learning by first training two individual models, one for the top view and another for the lateral position, and then moving the values to the relevant layers in the proposed multiview network design. Quantitative medical data has also been included into the training phase using a specific curriculum architecture that lets the model to initially learn from "easier" examples and then advance to "harder" examples to attain improved performance. Furthermore, our multiview network can work with both two simultaneous views as well as a single view as input. Using a database of 1,964 photographs for the classification of elbow fracture, we rigorously evaluate our methods. Results show that our technique may enhance the effectiveness of the tested methods and perform better than two comparable methods on broken bones investigation under various conditions.