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

arXiv 2209.09880 ↗First posted 20 Sep 2022eess.IV cs.CVcited by 0

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Abstract (arXiv)

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.

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References · 16

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  3. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,678
  4. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
  5. 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,543
  6. 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,250
  7. 2018Residual Dense Network for Image Super-Resolutioncited by 796
  8. 2018The relativistic discriminator: a key element missing from standard GANcited by 234
  9. 2016Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesiscited by 102
  10. 2016Medical image denoising using convolutional denoising autoencoderscited by 76
  11. 2019Recognition of Ischaemia and Infection in Diabetic Foot Ulcers: Dataset and Techniquescited by 19
  12. 2020Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluationcited by 19
4 more
  1. 2017Fully Convolutional Networks for Diabetic Foot Ulcer Segmentationcited by 17
  2. 2017DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classificationcited by 16
  3. 2021Analysis Towards Classification of Infection and Ischaemia of Diabetic Foot Ulcerscited by 16
  4. 2018Brain MRI super-resolution using 3D generative adversarial networkscited by 15

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