A complication caused by diabetes mellitus is the appearance of lesions in the foot region called Diabetic Foot Ulcers (DFU). Delayed treatment can lead to infection or ulcer ischemia, leading to lower limb amputation in an advanced stage. This article proposes the DFU-VGG, a convolutional neural network (CNN) inspired by convolutional blocks of VGG-19 but with smaller dense layers and batch normalizations operations. To specify the DFU-VGG parameters, we fine-tuned seven different CNN architectures using two image datasets containing 8,250 images with different color, contrast, resolution, and texture features. The proposed evaluation identifies f our classes: none, ischemia, infection, and both. Our approach achieved 93.45% of accuracy and an “excellent” Kappa index of 89.24%.