Attention-based multi-scale transfer ResNet for skull fracture image classification

The diagnosis of skull fracture is mainly judged by analyzing the scanned image of the skull. The diagnosis of skull fracture is essentially a special image classification problem. Recently, image classification methods based on deep learning have achieved good performance for general image classification. However, the effect of applying these methods to the diagnosis of skull fracture is not satisfactory. The reason is that it is difficult to distinguish the fracture regions from the background in the scanning image, and the extracted features of skull fracture and the background are very similar and indistinguishable. In order to solve the above problems, this paper proposed a novel skull fracture image classification approach based on attention mechanism, the proposed multi-scale transfer learning and residual network (ResNet), called attention-based multi-scale transfer ResNet (AMT-ResNet). In AMT-ResNet, attention mechanism is employed to give different focus to the feature information extracted by ResNet. In addition, the proposed multi-scale transfer learning is used to extract the common features from the multi-scale skull fracture images. Our proposed approach is evaluated on the datasets provided by Fujian medical university union hospital. Experimental results show that AMT-ResNet obtains better classification accuracy than other methods on skull fracture image classification.