In IEEE open journal of engineering in medicine and biology
Motivation: Infection (bacteria in the wound) and ischemia (insufficient blood supply) in Diabetic Foot Ulcers (DFUs) increase the risk of limb amputation. Goal: To develop an image-based DFU infection and ischemia detection system that uses deep learning. Methods: The DFU dataset was augmented using geometric and color image operations, after which binary infection and ischemia classification was done using the EfficientNet deep learning model and a comprehensive set of baselines. Results: The EfficientNets model achieved 99% accuracy in ischemia classification and 98% in infection classification, outperforming ResNet and Inception (87% accuracy) and Ensemble CNN, the prior state of the art (Classification accuracy of 90% for ischemia 73% for infection). EfficientNets also classified test images in a fraction (10% to 50%) of the time taken by baseline models. Conclusions: This work demonstrates that EfficientNets is a viable deep learning model for infection and ischemia classification.
Liu Ziyang, John Josvin, Agu Emmanuel
2022
Deep Learning, Diabetic Foot Ulcers, EfficientNet, Infection, Ischemia
