Diabetic Wound Segmentation using Convolutional Neural Networks

Cui, Can; Thurnhofer‐Hemsi, Karl; Soroushmehr, S. M. Reza; Mishra, Abinash; Gryak, Jonathan; Domínguez, Enrique; Najarian, Kayvan; López‐Rubio, Ezequiel

doi:10.1109/embc.2019.8856665

Cited by 33 publications

(24 citation statements)

References 12 publications

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“…The output result will be obtained as different stages of the wounded image, specifically the initial and final stages. Cui et al [28] presented their work on diabetic wound segmentation based on CNN. The diabetic foot ulcer image dataset provided by New York University was considered.…”

Section: Literature Surveymentioning

confidence: 99%

Exploiting Machine Learning Algorithms to Diagnose Foot Ulcers in Diabetic Patients

Reddy¹,

Mahesh²,

Nanjundan³

2021

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Diabetic foot ulcer (DFU) is a complication of diabetes that affects most of the diabetic patients.It will cause open wounds on the foot. Untreated DFU will lead to amputation and infection, which results in removal of foot or leg. As diabetes is the major health problem faced by people of all age groups, identifying foot ulcers at an early stage is essential. In this context, an efficient model to predict the foot ulcer accurately was proposed in this work. OBJECTIVES: To predict DFU using an effective neural network algorithm on a suitable dataset that consists of risk factors and clinical outcomes of the disease. METHODS: In recent days, ML techniques are most commonly used for predicting various diseases. To achieve the objectives a neural network technique, namely extreme learning machine (ELM) is proposed to predict DFU accurately. In addition, three existing algorithms, namely KNN, SVM with Gaussian kernel and ANN are also considered. These are implemented in R programming. RESULTS: Algorithms compared in terms of five evaluation metrics accuracy, zero-one loss, threat score/critical success index (TS/CSI), false omission rate (FOR) and false discovery rate (FDR). The values of accuracy, 0-1 loss, TS/CSI, FOR and FDR obtained for ELM are 96.15%, 0.0385, 0.95, 0 and 0.05 respectively. CONCLUSION: After comparison, it was discovered that ELM had outperformed other algorithms in terms of all the metrics. Thus, it was recommended to use ELM over other algorithms while predicting diabetic foot ulcers.

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Section: Literature Surveymentioning

confidence: 99%

Exploiting Machine Learning Algorithms to Diagnose Foot Ulcers in Diabetic Patients

Reddy¹,

Mahesh²,

Nanjundan³

2021

EAI Endorsed Trans Perv Health Tech

View full text Add to dashboard Cite

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“…Wang et al proposed to use the ConvNet [34], which is based on an encoder-decoder CNN architecture, and tested it on a data set containing diabetic foot ulcers that were manually pre-processed with a modified version of the GrabCut algorithm [35] to remove background. Similarly, other CNN architectures have been used, such as the U-Net [17,36] or the fully convolutional network (FCN) [37,38], all in combination with pre-processing steps that remove uninformative background either by the manual interaction of the user [36] or by manual features engineering to detect background pixels [15,38]. Standardizing the background in advance, before taking the picture, has also been a technical choice proposed in several works to keep the background consistent over different wound images.…”

Section: Segmenting Wound Imagesmentioning

confidence: 99%

Detect-and-Segment: a Deep Learning Approach to Automate Wound Image Segmentation

Scebba¹,

Zhang²,

Catanzaro³

et al. 2021

Preprint

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Chronic wounds significantly impact quality of life. If not properly managed, they can severely deteriorate. Image-based wound analysis could aid in objectively assessing the wound status by quantifying important features that are related to healing. However, the high heterogeneity of the wound types, image background composition, and capturing conditions challenge the robust segmentation of wound images. We present Detect-and-Segment (DS), a deep learning approach to produce wound segmentation maps with high generalization capabilities. In our approach, dedicated deep neural networks detected the wound position, isolated the wound from the uninformative background, and computed the wound segmentation map. We evaluated this approach using one data set with images of diabetic foot ulcers. For further testing, 4 supplemental independent data sets with larger variety of wound types from different body locations were used. The Matthews' correlation coefficient (MCC) improved from 0.29 when computing the segmentation on the full image to 0.85 when combining detection and segmentation in the same approach. When tested on the wound images drawn from the supplemental data sets, the DS approach increased the mean MCC from 0.17 to 0.85. Furthermore, the DS approach enabled the training of segmentation models with up to 90% less training data while maintaining the segmentation performance. KeywordsChronic wounds • ulcers • generalizability • out-of-distribution testing • deep learning • deep neural network • wound segmentation • wound detection • skin lesions.

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“…handwriting, face, behavior...), decision support systems and image classification. Recent research shows that deep networks are powerful tools for medical image analysis [19], [25]. Therefore, they offer great potential for melanoma classification [26], [27].…”

Section: Introductionmentioning

confidence: 99%

Skin Lesion Classification by Ensembles of Deep Convolutional Networks and Regularly Spaced Shifting

et al. 2021

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under grant RTI2018-094645-B-I00, project name Automated detection with low-cost hardware of unusual activities in video sequences. It is also partially supported by the Autonomous Government of Andalusia (Spain) under project UMA18-FEDERJA-084, project name Detection of anomalous behavior agents by deep learning in low-cost video surveillance intelligent systems. All of them include funds from the European Regional Development Fund (ERDF). It is also partially supported by the University of Malaga (Spain) under grants B1-2019_02, project name Self-Organizing Neural Systems for Non-Stationary Environments, and B1-2019_01, project name Anomaly detection on roads by moving cameras. The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the SCBI (Supercomputing and Bioinformatics) center of the University of Málaga. They also gratefully acknowledge the support of NVIDIA Corporation with the donation of two Titan X GPUs. The authors acknowledge the funding from the Universidad de Málaga. Funding for open access charge: Universidad de Málaga / CBUA.

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Diabetic Wound Segmentation using Convolutional Neural Networks

Cited by 33 publications

References 12 publications

Exploiting Machine Learning Algorithms to Diagnose Foot Ulcers in Diabetic Patients

Exploiting Machine Learning Algorithms to Diagnose Foot Ulcers in Diabetic Patients

Detect-and-Segment: a Deep Learning Approach to Automate Wound Image Segmentation

Skin Lesion Classification by Ensembles of Deep Convolutional Networks and Regularly Spaced Shifting

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