A Composite Model of Wound Segmentation Based on Traditional Methods and Deep Neural Networks

Figure 2

The architecture of our composite model. Raw images are preprocessed by skin with wound detection algorithm to remove the environmental backgrounds. Then, the training data composed of the preprocessed images and the raw images are normalized, cropped, and deformed. The DNN is trained to segment the testing data. At last, the segmented results are corrected semantically.