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

TL;DR

This paper explores deep learning techniques for enhancing diabetic foot ulcer images through noise reduction and super-resolution, aiming to improve early diagnosis and monitoring.

Contribution

It introduces the application of CNN-SAE for noise removal and evaluates four super-resolution models for diabetic foot ulcer image enhancement.

Findings

Noise reduction with CNN-SAE effectively removes Gaussian noise.

Super-resolution models improve image quality and detail.

Techniques are viable and easy to implement for DFU monitoring.

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.