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Diabetic foot ulcers monitoring by employing super resolution and noise reduction deep learning techniques

2022-09-20 17:35:49
Agapi Davradou, Eftychios Protopapadakis, Maria Kaselimi, Anastasios Doulamis, Nikolaos Doulamis
arXiv_CV
       
Abstract

Diabetic foot ulcers (DFUs) constitute a serious complication for people with diabetes. The care of DFU patients can be substantially improved through self-management, in order to achieve early-diagnosis, ulcer prevention, and complications management in existing ulcers. In this paper, we investigate two categories of image-to-image translation techniques (ItITT), which will support decision making and monitoring of diabetic foot ulcers: noise reduction and super-resolution. In the former case, we investigated the capabilities on noise removal, for convolutional neural network stacked-autoencoders (CNN-SAE). CNN-SAE was tested on RGB images, induced with Gaussian noise. The latter scenario involves the deployment of four deep learning super-resolution models. The performance of all models, for both scenarios, was evaluated in terms of execution time and perceived quality. Results indicate that applied techniques consist a viable and easy to implement alternative that should be used by any system designed for DFU monitoring.

Abstract (translated)

URL

https://arxiv.org/abs/2209.09880

PDF

https://arxiv.org/pdf/2209.09880.pdf

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