A Refined Deep Learning Architecture for Diabetic Foot Ulcers Detection
This addresses the need for computer-aided diagnosis systems to assist clinicians in early DFU detection, though it appears incremental as it refines an existing method.
The paper tackled the problem of detecting Diabetic Foot Ulcers (DFU) to prevent amputations by proposing a refined deep learning architecture based on EfficientDet, achieving improved detection on a dataset of 4,500 images.
Diabetic Foot Ulcers (DFU) that affect the lower extremities are a major complication of diabetes. Each year, more than 1 million diabetic patients undergo amputation due to failure to recognize DFU and get the proper treatment from clinicians. There is an urgent need to use a CAD system for the detection of DFU. In this paper, we propose using deep learning methods (EfficientDet Architectures) for the detection of DFU in the DFUC2020 challenge dataset, which consists of 4,500 DFU images. We further refined the EfficientDet architecture to avoid false negative and false positive predictions. The code for this method is available at https://github.com/Manugoyal12345/Yet-Another-EfficientDet-Pytorch.