A Superpixel-Wise Fully Convolutional Neural Network Approach for Diabetic Foot Ulcer Tissue Classification

Niri, Rania; Douzi, Hassan; Lucas, Yves; Treuillet, Sylvie

doi:10.1007/978-3-030-68763-2_23

Cited by 14 publications

(5 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Automated wound tissue analysis can help avoid contact with wounds and reduce the risk of infection. Niri et al [102] develop a smartphone-based ulcer tissue segmentation system. RoI segmentation is first performed using U-Net, followed by superpixel extraction of ROI using the simple linear iterative clustering (SLIC) algorithm.…”

Section: B Segmentation Of Dfus/diabetic Woundsmentioning

confidence: 99%

A Survey of Wound Image Analysis Using Deep Learning: Classification, Detection, and Segmentation

Zhang

Tian

et al. 2022

IEEE Access

View full text Add to dashboard Cite

Wounds not only harm the physical and mental health of patients, but also introduce huge medical costs. Meanwhile, there is a shortage of physicians in some areas, and clinical examinations are sometimes unreliable in wound diagnosis. Reliable wound analysis is of great importance in its diagnosis, treatment, and care. Currently, deep learning has developed rapidly in the field of computer vision and medical imaging and has become the most commonly used technique in wound image analysis. This paper studies the current research on deep learning in the field of wound image analysis, including classification, detection, and segmentation. We first review the publicly available datasets from various research, and study the preprocessing methods used in wound image analysis. Second, various models used in different deep learning tasks (classification, detection, and segmentation) and their applications in different types of wounds (e.g., burns, diabetic foot ulcers, pressure ulcers) are investigated. Finally, we discuss the challenges in the field of wound image analysis using deep learning, and provide an outlook on the research and development prospects.

show abstract

Section: B Segmentation Of Dfus/diabetic Woundsmentioning

confidence: 99%

A Survey of Wound Image Analysis Using Deep Learning: Classification, Detection, and Segmentation

Zhang

Tian

et al. 2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Further, research [8] has suggested a smartphone-oriented skin telemonitoring system for assisting in medical decisions and diagnosis while analyzing DFU tissues. The database encompassed 219 images.…”

Section: Introductionmentioning

confidence: 99%

Empowering Foot Health: Harnessing the Adaptive Weighted Sub-Gradient Convolutional Neural Network for Diabetic Foot Ulcer Classification

Alqahtani,

Alsubai,

Rahamathulla

et al. 2023

Diagnostics

View full text Add to dashboard Cite

In recent times, DFU (diabetic foot ulcer) has become a universal health problem that affects many diabetes patients severely. DFU requires immediate proper treatment to avert amputation. Clinical examination of DFU is a tedious process and complex in nature. Concurrently, DL (deep learning) methodologies can show prominent outcomes in the classification of DFU because of their efficient learning capacity. Though traditional systems have tried using DL-based models to procure better performance, there is room for enhancement in accuracy. Therefore, the present study uses the AWSg-CNN (Adaptive Weighted Sub-gradient Convolutional Neural Network) method to classify DFU. A DFUC dataset is considered, and several processes are involved in the present study. Initially, the proposed method starts with pre-processing, excluding inconsistent and missing data, to enhance dataset quality and accuracy. Further, for classification, the proposed method utilizes the process of RIW (random initialization of weights) and log softmax with the ASGO (Adaptive Sub-gradient Optimizer) for effective performance. In this process, RIW efficiently learns the shift of feature space between the convolutional layers. To evade the underflow of gradients, the log softmax function is used. When logging softmax with the ASGO is used for the activation function, the gradient steps are controlled. An adaptive modification of the proximal function simplifies the learning rate significantly, and optimal proximal functions are produced. Due to such merits, the proposed method can perform better classification. The predicted results are displayed on the webpage through the HTML, CSS, and Flask frameworks. The effectiveness of the proposed system is evaluated with accuracy, recall, F1-score, and precision to confirm its effectual performance.

show abstract

“…The best achieved accuracy reported was 98.39% using the SVM classifier. Researchers in study [21] took thermograms in controlled environments with a homogeneous background and used k-means clustering, and a further approach on every foot for foot segmentation. The identification of ulceration on the image was different pixel to pixel and a thresholding technique was used.…”

Section: Introductionmentioning

confidence: 99%

Automatic Diabetic Foot Ulcer Recognition Using Multi-Level Thermographic Image Data

et al. 2023

View full text Add to dashboard Cite

Lower extremity diabetic foot ulcers (DFUs) are a severe consequence of diabetes mellitus (DM). It has been estimated that people with diabetes have a 15% to 25% lifetime risk of acquiring DFUs which leads to the risk of lower limb amputations up to 85% due to poor diagnosis and treatment. Diabetic foot develops planter ulcers where thermography is used to detect the changes in the planter temperature. In this study, publicly available thermographic image data including both control group and diabetic group patients are used. Thermograms at image level as well as patch level are utilized for DFU detection. For DFU recognition, several machine-learning-based classification approaches are employed with hand-crafted features. Moreover, a couple of convolutional neural network models including ResNet50 and DenseNet121 are evaluated for DFU recognition. Finally, a CNN-based custom-developed model is proposed for the recognition task. The results are produced using image-level data, patch-level data, and image–patch combination data. The proposed CNN-based model outperformed the utilized models as well as the state-of-the-art models in terms of the AUC and accuracy. Moreover, the recognition accuracy for both the machine-learning and deep-learning approaches was higher for the image-level thermogram data in comparison to the patch-level or combination of image–patch thermograms.

show abstract

A Superpixel-Wise Fully Convolutional Neural Network Approach for Diabetic Foot Ulcer Tissue Classification

Cited by 14 publications

References 20 publications

A Survey of Wound Image Analysis Using Deep Learning: Classification, Detection, and Segmentation

A Survey of Wound Image Analysis Using Deep Learning: Classification, Detection, and Segmentation

Empowering Foot Health: Harnessing the Adaptive Weighted Sub-Gradient Convolutional Neural Network for Diabetic Foot Ulcer Classification

Automatic Diabetic Foot Ulcer Recognition Using Multi-Level Thermographic Image Data

Contact Info

Product

Resources

About