Clinical Assessment of Diabetic Foot Ulcers Using GWO-CNN based Hyperspectral Image Processing Approach

Devi, T. Arumuga Maria; Hepzibai, R.

doi:10.1080/03772063.2022.2099469

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…Wang et al also revealed that SVM could be effective in the classification of diabetic ulcers 37 . Jaganathan et al and Devi et al also revealed the utilization of the CNN algorithm in evaluating various wounds 38,39 . Based on the evidence, CNNs are at learning hierarchical features from images, capturing spatial hierarchies and complex patterns in surgical wound data 34 .…”

Section: Discussionmentioning

confidence: 99%

“… 37 Jaganathan et al and Devi et al also revealed the utilization of the CNN algorithm in evaluating various wounds. 38 , 39 Based on the evidence, CNNs are at learning hierarchical features from images, capturing spatial hierarchies and complex patterns in surgical wound data. 34 SVMs are well‐suited for binary classification tasks, such as determining whether a wound is infected, and perform well in high‐dimensional feature spaces.…”

Section: Discussionmentioning

confidence: 99%

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Risk predictions of surgical wound complications based on a machine learning algorithm: A systematic review

Zhang,

Zhao,

Farzan

et al. 2024

International Wound Journal

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Surgical wounds may arise due to harm inflicted upon soft tissue during surgical intervention, and many complications and injuries may accompany them. These complications can lead to prolonged hospitalization and poorer clinical outcomes. Also, Machine learning (ML) is a Section of artificial intelligence (AI) that has emerged in medical care and is increasingly used for diagnosis, complications, prognosis and recurrence prediction. This study aims to investigate surgical wound risk predictions and management using a ML algorithm by R programming language analysis. The systematic review, following PRISMA guidelines, spanned electronic databases using search terms like ‘machine learning’, ‘surgical’ and ‘wound’. Inclusion criteria covered experimental studies from 1990 to the present on ML's application in surgical wound evaluation. Exclusion criteria included studies lacking full text, focusing on ML in all surgeries, neglecting wound assessment and duplications. Two authors rigorously assessed titles, abstracts and full texts, excluding reviews and guidelines. Ultimately, relevant articles were then analysed. The present study identified nine articles employing ML for surgical wound management. The analysis encompassed various surgical procedures, including Cardiothoracic, Caesarean total abdominal colectomy, Burn plastic surgery, facial plastic surgery, laparotomy, minimal invasive surgery, hernia repair and unspecified surgeries. ML was skillful in evaluating surgical site infections (SSI) in seven studies, while two extended its use to burn‐grade diagnosis and wound classification. Support Vector Machine (SVM) and Convolutional Neural Network (CNN) were the most utilized algorithms. ANN achieved a 96% accuracy in facial plastic surgery wound management. CNN demonstrated commendable accuracies in various surgeries, and SVM exhibited high accuracy in multiple surgeries and burn plastic surgery. In sum, these findings underscore ML's potential for significant improvements in postoperative management and the development of enhanced care techniques, particularly in surgical wound management.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Risk predictions of surgical wound complications based on a machine learning algorithm: A systematic review

Zhang,

Zhao,

Farzan

et al. 2024

International Wound Journal

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“…They utilized the GWO to optimize the parameters of the backpropagation neural network, improving convergence and enhancing the model's performance. Arumuga et al [30] proposed the use of the GWO algorithm to optimize convolutional neural network models, effectively identifying areas of Diabetic Foot Ulcers. They compared the results with existing algorithms and found that the proposed algorithm improved accuracy.…”

Section: Neural Network Trainingmentioning

confidence: 99%

The Development and Application Research of Grey Wolf Optimization Algorithm

Chai,

Zhang,

Liu

2024

Frontiers in Artificial Intelligence and Applications

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The Grey Wolf Optimization (GWO) algorithm is an emerging swarm intelligence optimization technique known for its simplicity, minimal control parameters, fast convergence, and ease of implementation. This paper investigates the search mechanism and implementation process of the GWO algorithm, analyzing its shortcomings, including poor population diversity, slow convergence in later stages, and susceptibility to local optima. It provides an overview of improvement strategies for the Grey Wolf Optimization algorithm, encompassing enhancements to the population, parameters, search mechanism, and its integration with other optimization algorithms. Furthermore, the paper discusses the applications of the Grey Wolf Optimization algorithm in various domains.

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“…Lastly, the authors utilized robust and lightweight deep localization schemes from mobile devices for detecting the DFU on foot image to a remote monitor. In [20], a novel image processing system was presented for effectual calculation and classifier of DFU images.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Diabetic Foot Ulcer Detection and Classification Using Deep Learning

Nagaraju,

Kumar,

Rani

et al. 2023

IEEE Access

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Clinical Assessment of Diabetic Foot Ulcers Using GWO-CNN based Hyperspectral Image Processing Approach

Cited by 5 publications

References 23 publications

Risk predictions of surgical wound complications based on a machine learning algorithm: A systematic review

Risk predictions of surgical wound complications based on a machine learning algorithm: A systematic review

The Development and Application Research of Grey Wolf Optimization Algorithm

Automated Diabetic Foot Ulcer Detection and Classification Using Deep Learning

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