FusionSegNet: Fusing global foot features and local wound features to diagnose diabetic foot.

The Diabetic Foot (DF) is threatening every diabetic patient's health. Every year, more than one million people suffer amputation in the world due to lack of timely diagnosis of DF. Diagnosing DF at early stage is very essential to improve the survival rate and quality of patients. However, it is easy for inexperienced doctors to confuse DFU wounds and other specific ulcer wounds when there is a lack of patients' health records in underdeveloped areas. It is of great value to distinguish diabetic foot ulcer from chronic wounds. And the characteristics of deep learning can be well applied in this field. In this paper, we propose the FusionSegNet fusing global foot features and local wound features to identify DF images from foot ulcer images. In particular, we apply a wound segmentation module to segment foot ulcer wounds, which guides the network to pay attention to wound area. T he FusionSegNet combines two kinds of features to make a final prediction. Our method is evaluated upon our dataset collected by Shanghai Municipal Eighth People's Hospital in clinical environment. In the training-validation stage, we collect 1211 images for a 5-fold cross-validation. Our method can classify DF images and non-DF images with the area under the receiver operating characteristic curve (AUC) value of 98.93%, accuracy of 95.78%, sensitivity of 94.27%, specificity of 96.88%, and F1-score of 94.91%. With the excellent performance, the proposed method can accurately extract wound features and greatly improve the classification performance. In general, the method proposed in this paper can help clinicians make more accurate judgments of diabetic foot and has great potential in clinical auxiliary diagnosis.