Diabetic foot ulcers monitoring by employing super resolution and noise
reduction deep learning techniques
- URL: http://arxiv.org/abs/2209.09880v1
- Date: Tue, 20 Sep 2022 17:35:49 GMT
- Title: Diabetic foot ulcers monitoring by employing super resolution and noise
reduction deep learning techniques
- Authors: Agapi Davradou, Eftychios Protopapadakis, Maria Kaselimi, Anastasios
Doulamis, Nikolaos Doulamis
- Abstract summary: We investigate two categories of image-to-image translation techniques (ItITT), which will support decision making and monitoring of diabetic foot ulcers.
In the former case, we investigated the capabilities on noise removal, for convolutional neural network stacked-autoencoders (CNN-SAE)
The latter scenario involves the deployment of four deep learning super-resolution models.
- Score: 10.837348673297083
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Rotational Augmented Noise2Inverse for Low-dose Computed Tomography
Reconstruction [83.73429628413773]
Supervised deep learning methods have shown the ability to remove noise in images but require accurate ground truth.
We propose a novel self-supervised framework for LDCT, in which ground truth is not required for training the convolutional neural network (CNN)
Numerical and experimental results show that the reconstruction accuracy of N2I with sparse views is degrading while the proposed rotational augmented Noise2Inverse (RAN2I) method keeps better image quality over a different range of sampling angles.
arXiv Detail & Related papers (2023-12-19T22:40:51Z) - Improving Classification of Retinal Fundus Image Using Flow Dynamics
Optimized Deep Learning Methods [0.0]
Diabetic Retinopathy (DR) refers to a barrier that takes place in diabetes mellitus damaging the blood vessel network present in the retina.
It can take some time to perform a DR diagnosis using color fundus pictures because experienced clinicians are required to identify the tumors in the imagery used to identify the illness.
arXiv Detail & Related papers (2023-04-29T16:11:34Z) - DRAC: Diabetic Retinopathy Analysis Challenge with Ultra-Wide Optical
Coherence Tomography Angiography Images [51.27125547308154]
We organized a challenge named "DRAC - Diabetic Retinopathy Analysis Challenge" in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022)
The challenge consists of three tasks: segmentation of DR lesions, image quality assessment and DR grading.
This paper presents a summary and analysis of the top-performing solutions and results for each task of the challenge.
arXiv Detail & Related papers (2023-04-05T12:04:55Z) - The role of noise in denoising models for anomaly detection in medical
images [62.0532151156057]
Pathological brain lesions exhibit diverse appearance in brain images.
Unsupervised anomaly detection approaches have been proposed using only normal data for training.
We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes.
arXiv Detail & Related papers (2023-01-19T21:39:38Z) - Segmentation, Classification, and Quality Assessment of UW-OCTA Images
for the Diagnosis of Diabetic Retinopathy [2.435307010444828]
Diabetic Retinopathy (DR) is a severe complication of diabetes that can cause blindness.
In this paper, we will present our solutions for the three tasks of the Diabetic Retinopathy Analysis Challenge 2022 (DRAC22)
The obtained results are promising and have allowed us to position ourselves in the TOP 5 of the segmentation task.
arXiv Detail & Related papers (2022-11-21T14:49:18Z) - Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis [68.5287824124996]
We present a new type of discriminator, the segmentor, to accurately locate the lesions and improve the visual quality of pseudo-healthy images.
We apply the generated images into medical image enhancement and utilize the enhanced results to cope with the low contrast problem.
Comprehensive experiments on the T2 modality of BraTS demonstrate that the proposed method substantially outperforms the state-of-the-art methods.
arXiv Detail & Related papers (2022-03-29T08:41:17Z) - Blindness (Diabetic Retinopathy) Severity Scale Detection [0.0]
Diabetic retinopathy (DR) is a severe complication of diabetes that can cause permanent blindness.
Timely diagnosis and treatment of DR are critical to avoid total loss of vision.
We propose a novel deep learning based method for automatic screening of retinal fundus images.
arXiv Detail & Related papers (2021-10-04T11:31:15Z) - A Machine Learning Model for Early Detection of Diabetic Foot using
Thermogram Images [3.8261286462270006]
Diabetes foot ulceration (DFU) and amputation are a cause of significant morbidity.
thermogram images may help to detect an increase in plantar temperature prior to DFU.
We propose a robust solution to identify the diabetic foot.
arXiv Detail & Related papers (2021-06-27T11:37:59Z) - An Interpretable Multiple-Instance Approach for the Detection of
referable Diabetic Retinopathy from Fundus Images [72.94446225783697]
We propose a machine learning system for the detection of referable Diabetic Retinopathy in fundus images.
By extracting local information from image patches and combining it efficiently through an attention mechanism, our system is able to achieve high classification accuracy.
We evaluate our approach on publicly available retinal image datasets, in which it exhibits near state-of-the-art performance.
arXiv Detail & Related papers (2021-03-02T13:14:15Z) - Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis
and Uncertainty Quantification [0.0]
Diabetic Retinopathy (DR) is one of the microvascular complications of Diabetes Mellitus, which remains one of the leading causes of blindness worldwide.
Computational models based on Conal Neural Networks represent the state of the art for the automatic detection of DR using eye fundus images.
In this paper, a hybrid Deep Learning-Gaussian process method for DR diagnosis and uncertainty quantification is presented.
arXiv Detail & Related papers (2020-07-29T04:10:42Z) - Improved Slice-wise Tumour Detection in Brain MRIs by Computing
Dissimilarities between Latent Representations [68.8204255655161]
Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods.
We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder.
We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
arXiv Detail & Related papers (2020-07-24T14:02:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.