Diabetic Foot Ulcer Ischemia and Infection Classification Using EfficientNet Deep Learning Models

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.