Recognition of ischaemia and infection in diabetic foot ulcer: A deep convolutional neural network based approach

Diabetic foot ulcers (DFUs) result in amputation of lower limbs or feet without timely assessment and treatment. The assessment of DFUs is performed by the diagnosis of DFU ischaemia and infection. In this work, a new deep convolutional neural network (CNN) based approach (ResKNet) is proposed to perform such assessment. The proposed network consists of a series of unique residual blocks of 2D convolution, batch normalization, and LeakyReLU with skip connections. It has been experimentally observed that a shallower network with 4 such unique residual blocks (Res4Net) can achieve promising results in ischaemia recognition, with an impressive AUC value of 0.9968. However in case of infection recognition, a network with 7 such residual blocks (Res7Net) achieved the best performance, with an AUC value of 0.8890. Such improvements to the current state‐of‐the‐art results suggests that the proposed approach can provide significant help to medical experts, for automatic assessment of DFU.