DFootNet:使用密集神经网络架构的糖尿病足溃疡领域自适应分类框架

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Nishu Bansal, Ankit Vidyarthi
{"title":"DFootNet:使用密集神经网络架构的糖尿病足溃疡领域自适应分类框架","authors":"Nishu Bansal, Ankit Vidyarthi","doi":"10.1007/s12559-024-10282-4","DOIUrl":null,"url":null,"abstract":"<p>Diabetic foot ulcers (DFUs) are a prevalent and serious complication of diabetes, often leading to severe morbidity and even amputations if not timely diagnosed and managed. The increasing prevalence of DFUs poses a significant challenge to healthcare systems worldwide. Accurate and timely classification of DFUs is crucial for effective treatment and prevention of complications. In this paper, we present “DFootNet”, an innovative and comprehensive classification framework for the accurate assessment of diabetic foot ulcers using a dense neural network architecture. Our proposed approach leverages the power of deep learning to automatically extract relevant features from diverse clinical DFU images. The proposed model comprises a multi-layered dense neural network designed to handle the intricate patterns and variations present in different stages and types of DFUs. The network architecture integrates convolutional and fully connected layers, allowing for hierarchical feature extraction and robust feature representation. To evaluate the efficacy of DFootNet, we conducted experiments on a large and diverse dataset of diabetic foot ulcers. Our results demonstrate that DFootNet achieves a remarkable accuracy of 98.87%, precision—99.01%, recall—98.73%, F1-score as 98.86%, and AUC-ROC as 98.13%, outperforming existing methods in distinguishing between ulcer and non-ulcer images. Moreover, our framework provides insights into the decision-making process, offering transparency and interpretability through attention mechanisms that highlight important regions within ulcer images. We also present a comparative analysis of DFootNet’s performance against other popular deep learning models, showcasing its robustness and adaptability across various scenarios.</p>","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"DFootNet: A Domain Adaptive Classification Framework for Diabetic Foot Ulcers Using Dense Neural Network Architecture\",\"authors\":\"Nishu Bansal, Ankit Vidyarthi\",\"doi\":\"10.1007/s12559-024-10282-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Diabetic foot ulcers (DFUs) are a prevalent and serious complication of diabetes, often leading to severe morbidity and even amputations if not timely diagnosed and managed. The increasing prevalence of DFUs poses a significant challenge to healthcare systems worldwide. Accurate and timely classification of DFUs is crucial for effective treatment and prevention of complications. In this paper, we present “DFootNet”, an innovative and comprehensive classification framework for the accurate assessment of diabetic foot ulcers using a dense neural network architecture. Our proposed approach leverages the power of deep learning to automatically extract relevant features from diverse clinical DFU images. The proposed model comprises a multi-layered dense neural network designed to handle the intricate patterns and variations present in different stages and types of DFUs. The network architecture integrates convolutional and fully connected layers, allowing for hierarchical feature extraction and robust feature representation. To evaluate the efficacy of DFootNet, we conducted experiments on a large and diverse dataset of diabetic foot ulcers. Our results demonstrate that DFootNet achieves a remarkable accuracy of 98.87%, precision—99.01%, recall—98.73%, F1-score as 98.86%, and AUC-ROC as 98.13%, outperforming existing methods in distinguishing between ulcer and non-ulcer images. Moreover, our framework provides insights into the decision-making process, offering transparency and interpretability through attention mechanisms that highlight important regions within ulcer images. We also present a comparative analysis of DFootNet’s performance against other popular deep learning models, showcasing its robustness and adaptability across various scenarios.</p>\",\"PeriodicalId\":51243,\"journal\":{\"name\":\"Cognitive Computation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-04-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cognitive Computation\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s12559-024-10282-4\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cognitive Computation","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s12559-024-10282-4","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

糖尿病足溃疡(DFUs)是一种普遍而严重的糖尿病并发症,如果得不到及时诊断和治疗,往往会导致严重的发病率,甚至截肢。糖尿病足溃疡发病率的不断上升给全球医疗系统带来了巨大挑战。对 DFU 进行准确及时的分类对于有效治疗和预防并发症至关重要。在本文中,我们介绍了 "DFootNet",这是一个创新的综合分类框架,利用密集神经网络架构对糖尿病足溃疡进行准确评估。我们提出的方法利用深度学习的强大功能,自动从不同的临床 DFU 图像中提取相关特征。所提议的模型由多层密集神经网络组成,旨在处理不同阶段和类型的糖尿病足溃疡中存在的复杂模式和变化。该网络架构整合了卷积层和全连接层,可进行分层特征提取和稳健特征表示。为了评估 DFootNet 的功效,我们在一个大型、多样化的糖尿病足溃疡数据集上进行了实验。结果表明,在区分溃疡和非溃疡图像方面,DFootNet 的准确率为 98.87%,精确率为 99.01%,召回率为 98.73%,F1 分数为 98.86%,AUC-ROC 为 98.13%,均优于现有方法。此外,我们的框架还为决策过程提供了洞察力,通过关注机制突出了溃疡图像中的重要区域,从而提供了透明度和可解释性。我们还对 DFootNet 的性能与其他流行的深度学习模型进行了对比分析,展示了它在各种场景下的鲁棒性和适应性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

DFootNet: A Domain Adaptive Classification Framework for Diabetic Foot Ulcers Using Dense Neural Network Architecture

DFootNet: A Domain Adaptive Classification Framework for Diabetic Foot Ulcers Using Dense Neural Network Architecture

Diabetic foot ulcers (DFUs) are a prevalent and serious complication of diabetes, often leading to severe morbidity and even amputations if not timely diagnosed and managed. The increasing prevalence of DFUs poses a significant challenge to healthcare systems worldwide. Accurate and timely classification of DFUs is crucial for effective treatment and prevention of complications. In this paper, we present “DFootNet”, an innovative and comprehensive classification framework for the accurate assessment of diabetic foot ulcers using a dense neural network architecture. Our proposed approach leverages the power of deep learning to automatically extract relevant features from diverse clinical DFU images. The proposed model comprises a multi-layered dense neural network designed to handle the intricate patterns and variations present in different stages and types of DFUs. The network architecture integrates convolutional and fully connected layers, allowing for hierarchical feature extraction and robust feature representation. To evaluate the efficacy of DFootNet, we conducted experiments on a large and diverse dataset of diabetic foot ulcers. Our results demonstrate that DFootNet achieves a remarkable accuracy of 98.87%, precision—99.01%, recall—98.73%, F1-score as 98.86%, and AUC-ROC as 98.13%, outperforming existing methods in distinguishing between ulcer and non-ulcer images. Moreover, our framework provides insights into the decision-making process, offering transparency and interpretability through attention mechanisms that highlight important regions within ulcer images. We also present a comparative analysis of DFootNet’s performance against other popular deep learning models, showcasing its robustness and adaptability across various scenarios.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Cognitive Computation
Cognitive Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-NEUROSCIENCES
CiteScore
9.30
自引率
3.70%
发文量
116
审稿时长
>12 weeks
期刊介绍: Cognitive Computation is an international, peer-reviewed, interdisciplinary journal that publishes cutting-edge articles describing original basic and applied work involving biologically-inspired computational accounts of all aspects of natural and artificial cognitive systems. It provides a new platform for the dissemination of research, current practices and future trends in the emerging discipline of cognitive computation that bridges the gap between life sciences, social sciences, engineering, physical and mathematical sciences, and humanities.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信