Dense Dilated Attentive Network for Automatic Classification of Femur Trochanteric Fracture

Abstract

Automatic classification of femur trochanteric fracture is very valuable in clinical diagnosis practice. However, developing a high classification performance system is still challenging due to the various locations, shapes, and contextual information of the fracture regions. To tackle this challenge, we propose a novel dense dilated attentive (DDA) network for more accurate classification of 31A1/31A2/31A3 fractures from the X-ray images by incorporating a DDA layer. By exploiting this layer, the multiscale, contextual, and attentive features are encoded from different depths of the network and thus improving the feature learning ability of the classification network to gain a better classification performance. To validate the effectiveness of the DDA network, we conduct extensive experiments on the annotated femur trochanteric fracture data samples, and the experimental results demonstrate that the proposed DDA network could achieve competitive classification compared with other methods.