Improved diabetic retinopathy severity classification using squeeze-and-excitation and sparse light weight multi-level attention u-net with transfer learning from xception.

IF 3.1 3区 医学 Q2 ENDOCRINOLOGY & METABOLISM Acta Diabetologica Pub Date : 2024-07-26 DOI:10.1007/s00592-024-02341-x
Sachin Bhandari, Sunil Pathak, Sonal Amit Jain, Basant Agarwal
{"title":"Improved diabetic retinopathy severity classification using squeeze-and-excitation and sparse light weight multi-level attention u-net with transfer learning from xception.","authors":"Sachin Bhandari, Sunil Pathak, Sonal Amit Jain, Basant Agarwal","doi":"10.1007/s00592-024-02341-x","DOIUrl":null,"url":null,"abstract":"<p><strong>Aims: </strong>Diabetic Retinopathy (DR) is a significant cause of vision loss in diabetic patients, making early detection and accurate severity classification essential for effective management and prevention. This study aims to develop an enhanced DR severity classification approach using advanced model architectures and transfer learning to improve diagnostic accuracy and support better patient care.</p><p><strong>Methods: </strong>We propose a novel model, Xception Squeeze-and-Excitation Sparse Lightweight Multi-Level Attention U-Net (XceSE_SparseLwMLA-UNet), designed to classify DR severity using fundus images from the Messidor 1 and Messidor 2 datasets. The XceSE_SparseLwMLA-UNet integrates several advanced mechanisms: the Squeeze-and-Excitation (SE) mechanism for adaptive feature recalibration, the Sparse Lightweight Multi-Level Attention (SparseLwMLA) mechanism for effective contextual information integration, and transfer learning from the Xception architecture to enhance feature extraction capabilities. The SE mechanism refines channel-wise feature responses, while SparseLwMLA enhances the model's ability to identify complex DR patterns. Transfer learning utilizes pre-trained weights from Xception to improve generalization across DR severity levels.</p><p><strong>Results: </strong>The proposed XceSE_SparseLwMLA-UNet model demonstrates superior performance in DR severity classification, achieving higher accuracy and improved multi-class F1 scores compared to existing models. The model's color-coded segmentation outputs offer interpretable visual representations, aiding medical professionals in assessing DR severity levels.</p><p><strong>Conclusions: </strong>The XceSE_SparseLwMLA-UNet model shows promise for advancing early DR diagnosis and management by enhancing classification accuracy and providing valuable visual insights. Its integration of advanced architectural features and transfer learning contributes to better patient care and improved visual health outcomes.</p>","PeriodicalId":6921,"journal":{"name":"Acta Diabetologica","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Acta Diabetologica","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00592-024-02341-x","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
引用次数: 0

Abstract

Aims: Diabetic Retinopathy (DR) is a significant cause of vision loss in diabetic patients, making early detection and accurate severity classification essential for effective management and prevention. This study aims to develop an enhanced DR severity classification approach using advanced model architectures and transfer learning to improve diagnostic accuracy and support better patient care.

Methods: We propose a novel model, Xception Squeeze-and-Excitation Sparse Lightweight Multi-Level Attention U-Net (XceSE_SparseLwMLA-UNet), designed to classify DR severity using fundus images from the Messidor 1 and Messidor 2 datasets. The XceSE_SparseLwMLA-UNet integrates several advanced mechanisms: the Squeeze-and-Excitation (SE) mechanism for adaptive feature recalibration, the Sparse Lightweight Multi-Level Attention (SparseLwMLA) mechanism for effective contextual information integration, and transfer learning from the Xception architecture to enhance feature extraction capabilities. The SE mechanism refines channel-wise feature responses, while SparseLwMLA enhances the model's ability to identify complex DR patterns. Transfer learning utilizes pre-trained weights from Xception to improve generalization across DR severity levels.

Results: The proposed XceSE_SparseLwMLA-UNet model demonstrates superior performance in DR severity classification, achieving higher accuracy and improved multi-class F1 scores compared to existing models. The model's color-coded segmentation outputs offer interpretable visual representations, aiding medical professionals in assessing DR severity levels.

Conclusions: The XceSE_SparseLwMLA-UNet model shows promise for advancing early DR diagnosis and management by enhancing classification accuracy and providing valuable visual insights. Its integration of advanced architectural features and transfer learning contributes to better patient care and improved visual health outcomes.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用挤压-激发和稀疏轻权多层次注意力 U 网以及 xception 的迁移学习改进糖尿病视网膜病变严重程度分类。
目的:糖尿病视网膜病变(DR)是导致糖尿病患者视力丧失的重要原因,因此早期检测和准确的严重程度分类对于有效管理和预防至关重要。本研究旨在利用先进的模型架构和迁移学习,开发一种增强型糖尿病严重程度分类方法,以提高诊断准确性并支持更好的患者护理:我们提出了一种新型模型--Xception Squeeze-and-Excitation Sparse Lightweight Multi-Level Attention U-Net (XceSE_SparseLwMLA-UNet),旨在使用 Messidor 1 和 Messidor 2 数据集的眼底图像对 DR 严重程度进行分类。XceSE_SparseLwMLA-UNet 集成了几种先进的机制:用于自适应特征重新校准的挤压激励(SE)机制、用于有效整合上下文信息的稀疏轻量级多层次注意(SparseLwMLA)机制,以及用于增强特征提取能力的 Xception 架构迁移学习。SE 机制完善了信道特征响应,而 SparseLwMLA 则增强了模型识别复杂 DR 模式的能力。迁移学习利用来自 Xception 的预训练权重来提高 DR 严重程度的泛化能力:结果:与现有模型相比,所提出的 XceSE_SparseLwMLA-UNet 模型在 DR 严重程度分类方面表现出色,获得了更高的准确率和更好的多类 F1 分数。该模型的彩色编码分割输出提供了可解释的可视化表示,有助于医疗专业人员评估 DR 的严重程度:结论:XceSE_SparseLwMLA-UNet 模型通过提高分类准确性和提供有价值的可视化见解,有望推动早期 DR 诊断和管理。它整合了先进的架构功能和迁移学习,有助于改善患者护理和视觉健康结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Acta Diabetologica
Acta Diabetologica 医学-内分泌学与代谢
CiteScore
7.30
自引率
2.60%
发文量
180
审稿时长
2 months
期刊介绍: Acta Diabetologica is a journal that publishes reports of experimental and clinical research on diabetes mellitus and related metabolic diseases. Original contributions on biochemical, physiological, pathophysiological and clinical aspects of research on diabetes and metabolic diseases are welcome. Reports are published in the form of original articles, short communications and letters to the editor. Invited reviews and editorials are also published. A Methodology forum, which publishes contributions on methodological aspects of diabetes in vivo and in vitro, is also available. The Editor-in-chief will be pleased to consider articles describing new techniques (e.g., new transplantation methods, metabolic models), of innovative importance in the field of diabetes/metabolism. Finally, workshop reports are also welcome in Acta Diabetologica.
期刊最新文献
Predictors of Successful Antibiotic Treatment of Osteomyelitis in Diabetic Forefoot Infection. Time-dependent electroretinogram, metabolomics and lipidomics screened metabolic markers for monitoring the process of diabetic retinopathy from scratch. Revisiting the markers interleukin-6 and glucagon-like peptide-1 for targeting low-grade inflammation in type 2 diabetes: a meta-analysis and our lab experience. Expert panel opinion on the optimal educational pathway for diabetes educators for training people with type 1 diabetes on the MiniMed™ 780G system: a Delphi consensus. Identification and characterization of a case of mild familial partial lipodystrophy in a carrier of a LMNA p.Arg582Leu variant.
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1