FusionSegNet: Fusing global foot features and local wound features to diagnose diabetic foot

Lan, Tiancai; Li, Zhiwei

doi:10.1016/j.compbiomed.2022.106456

Cited by 9 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The low-frequency information corresponds to the overall shape of the wound, while the high-frequency information corresponds to the details of the wound. The combination of high and low-frequency information from wound images provides a more comprehensive wound characterization, which is essential for wound classification [36].…”

Section: Proposed Architecturementioning

confidence: 99%

Multi-Class Wound Classification via High and Low-Frequency Guidance Network

Guo,

Yi,

Dong

et al. 2023

Bioengineering

View full text Add to dashboard Cite

Wound image classification is a crucial preprocessing step to many intelligent medical systems, e.g., online diagnosis and smart medical. Recently, Convolutional Neural Network (CNN) has been widely applied to the classification of wound images and obtained promising performance to some extent. Unfortunately, it is still challenging to classify multiple wound types due to the complexity and variety of wound images. Existing CNNs usually extract high- and low-frequency features at the same convolutional layer, which inevitably causes information loss and further affects the accuracy of classification. To this end, we propose a novel High and Low-frequency Guidance Network (HLG-Net) for multi-class wound classification. To be specific, HLG-Net contains two branches: High-Frequency Network (HF-Net) and Low-Frequency Network (LF-Net). We employ pre-trained models ResNet and Res2Net as the feature backbone of the HF-Net, which makes the network capture the high-frequency details and texture information of wound images. To extract much low-frequency information, we utilize a Multi-Stream Dilation Convolution Residual Block (MSDCRB) as the backbone of the LF-Net. Moreover, a fusion module is proposed to fully explore informative features at the end of these two separate feature extraction branches, and obtain the final classification result. Extensive experiments demonstrate that HLG-Net can achieve maximum accuracy of 98.00%, 92.11%, and 82.61% in two-class, three-class, and four-class wound image classifications, respectively, which outperforms the previous state-of-the-art methods.

show abstract

Section: Proposed Architecturementioning

confidence: 99%

Multi-Class Wound Classification via High and Low-Frequency Guidance Network

Guo,

Yi,

Dong

et al. 2023

Bioengineering

View full text Add to dashboard Cite

show abstract

“…In conv2d_1 = (none, 75, 75, 32), conv2d_2 = (none, 38, 38, 64), conv2d_3 = (none, 19,19,128) layers. In max_pooling2d = (none, 38, 38, 32), max_pooling2d_1 = (none, 19,19,64), max_pooling2d_3 = (none, 10,10,128). The convolutional neural networks (CNNs) consist of 2D matrix features where each of the features is weighted as a sum of the input pixels.…”

Section: Introductionmentioning

confidence: 99%

“…The endorsed system classified the non-DF and DF images with a better AUC (area under receiver operating characteristics) rate. With such an ideal performance, the suggested methodology can extract the wound features better, thereby improving classification performance [10]. Though conventional models have endeavored to work better in DFU classification, there is a scope for enhancement in accordance with the accuracy rate.…”

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

An overview of current developments and methods for identifying diabetic foot ulcers: A survey

Anbarasi,

Jawahar,

Jayakumari

et al. 2024

WIREs Data Min & Knowl

View full text Add to dashboard Cite

Diabetic foot ulcers (DFUs) present a substantial health risk across diverse age groups, creating challenges for healthcare professionals in the accurate classification and grading. DFU plays a crucial role in automated health monitoring and diagnosis systems, where the integration of medical imaging, computer vision, statistical analysis, and gait information is essential for comprehensive understanding and effective management. Diagnosing DFU is imperative, as it plays a major role in the processes of diagnosis, treatment planning, and neuropathy research within automated health monitoring and diagnosis systems. To address this, various machine learning and deep learning‐based methodologies have emerged in the literature to support healthcare practitioners in achieving improved diagnostic analyses for DFU. This survey paper investigates various diagnostic methodologies for DFU, spanning traditional statistical approaches to cutting‐edge deep learning techniques. It systematically reviews key stages involved in diabetic foot ulcer classification (DFUC) methods, including preprocessing, feature extraction, and classification, explaining their benefits and drawbacks. The investigation extends to exploring state‐of‐the‐art convolutional neural network models tailored for DFUC, involving extensive experiments with data augmentation and transfer learning methods. The overview also outlines datasets commonly employed for evaluating DFUC methodologies. Recognizing that neuropathy and reduced blood flow in the lower limbs might be caused by atherosclerotic blood vessels, this paper provides recommendations to researchers and practitioners involved in routine medical therapy to prevent substantial complications. Apart from reviewing prior literature, this survey aims to influence the future of DFU diagnostics by outlining prospective research directions, particularly in the domains of personalized and intelligent healthcare. Finally, this overview is to contribute to the continual evolution of DFU diagnosis in order to provide more effective and customized medical care.This article is categorized under: Application Areas > Health Care Technologies > Machine Learning Technologies > Artificial Intelligence

show abstract

FusionSegNet: Fusing global foot features and local wound features to diagnose diabetic foot

Cited by 9 publications

References 24 publications

Multi-Class Wound Classification via High and Low-Frequency Guidance Network

Multi-Class Wound Classification via High and Low-Frequency Guidance Network

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

An overview of current developments and methods for identifying diabetic foot ulcers: A survey

Contact Info

Product

Resources

About