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Diabetes affects roughly 537 million people in the world, and it is predicted to reach 783 million by 2045. Diabetic Foot Ulcer (DFU) is a major issue with diabetes that may lead to lower limb amputation. The rapid evolution of DFU demands immediate intervention to prevent the terrible consequences of amputation and related complications.This research introduces a novel approach utilizing deep neural networks and machine learning for the accurate classification of diabetic foot ulcer (DFU) images. The proposed method harnesses the cutting-edge capabilities of Convolutional Neural Networks (CNN) and Vision Image Transformers (ViT) within a Siamese Neural Network (SNN) Architecture. By employing similarity learning, the model efficiently categorizes DFU images into four distinct classes: None, Infection, Ischemia, or Both. The training process involves the use of the DFU2021 dataset, with all ethical clearances duly obtained. Notably, the model exhibits remarkable performance on both the validation and test data, indicating a significant breakthrough in the field of DFU disease image classification. The potential of this innovative model extends beyond classification; it holds promise as an integral component of a comprehensive detection tool and longitudinal treatment protocol validation for DFU disease.

DFU-SIAM a Novel Diabetic Foot Ulcer Classification with Deep Learning