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

ABSTRACT: 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). Effici

SUBMITTER: Liu Z 

PROVIDER: S-EPMC9842228 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature