用于早期糖尿病视网膜病变检测和严重程度分类的新型自动系统

IF 2.3 4区 化学 Q1 SOCIAL WORK Journal of Chemometrics Pub Date : 2024-08-19 DOI:10.1002/cem.3593
Santoshkumar S Ainapur, Virupakshappa Patil
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引用次数: 0

摘要

糖尿病是一种常见的全球性严重疾病,它会损害眼部血管,导致视力下降。对这一问题进行早期准确诊断对于降低视力损伤风险至关重要。用于糖尿病视网膜病变(DR)分级的典型深度学习(DL)方法往往耗时较长,而且由于病变特征的表征不充分,导致检测性能不尽如人意。为了克服这些挑战,本研究提出了一种新的自动检测和分级 DR 的机制,旨在识别 DR 的严重程度和不同阶段。为了找出并捕捉 DR 样本的特征,在一个集合网模型中利用了共轭注意力机制和视觉转换器,自动生成用于诊断 DR 的特征图。然后,通过融合注意力网络模型中的特征融合功能将这些提取的特征图进行融合,计算注意力权重以生成最强大的特征图。最后,使用核极端学习机(KELM)模型识别和区分 DR 病例。为了评估 DR 的严重程度,我们的工作使用了四个不同的基准数据集:APTOS 2019、MESSIDOR-2 数据集、DiaRetDB1 V2.1 和 DIARETDB0 数据集。为了消除数据噪声和不必要的变化,进行了两个预处理步骤,包括对比度增强和光照校正。使用知名指标评估的实验结果表明,与其他基线方法相比,建议的方法达到了 99.63% 的较高准确率。这项研究有助于开发功能强大的 DR 筛选技术,这种技术耗时少,能够自动识别过早出现的 DR 严重程度。
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A Novel Automated System for Early Diabetic Retinopathy Detection and Severity Classification

Diabetes is a common and serious global disease that damages blood vessels in the eye, leading to vision loss. Early and accurate diagnosis of this issue is crucial to reduce the risk of visual impairment. The typical deep learning (DL) methods for diabetic retinopathy (DR) grading are often time-consuming, resulting in unsatisfactory detection performance due to inadequate representation of lesion features. To overcome these challenges, this research proposes a new automated mechanism for detecting and classifying DR, aiming to identify DR severities and different stages. To figure out and capture feature characteristics from DR samples, a conjugated attention mechanism and vision transformer are utilized within a collective net model, which automatically generates feature maps for diagnosing DR. These extracted feature maps are then fused through the feature fusion function in a fused attention net model, calculating attention weights to produce the most powerful feature map. Finally, the DR cases are identified and discriminated using the kernel extreme learning machine (KELM) model. For evaluating DR severity, our work utilizes four different benchmark datasets: APTOS 2019, MESSIDOR-2 dataset, DiaRetDB1 V2.1, and DIARETDB0 datasets. To illuminate data noise and unwanted variations, two preprocessing steps are carried out, which include contrast enhancement and illumination correction. The experimental results evaluated using well-known indicators demonstrate that the suggested method achieves a higher accuracy of 99.63% compared to other baseline methods. This research contributes to the development of powerful DR screening techniques that are less time-consuming and capable of automatically identifying DR severity levels at a premature level.

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来源期刊
Journal of Chemometrics
Journal of Chemometrics 化学-分析化学
CiteScore
5.20
自引率
8.30%
发文量
78
审稿时长
2 months
期刊介绍: The Journal of Chemometrics is devoted to the rapid publication of original scientific papers, reviews and short communications on fundamental and applied aspects of chemometrics. It also provides a forum for the exchange of information on meetings and other news relevant to the growing community of scientists who are interested in chemometrics and its applications. Short, critical review papers are a particularly important feature of the journal, in view of the multidisciplinary readership at which it is aimed.
期刊最新文献
Issue Information Cover Image Past, Present and Future of Research in Analytical Figures of Merit Analytical Figures of Merit in Univariate, Multivariate, and Multiway Calibration: What Have We Learned? What Do We Still Need to Learn? Paul Geladi (1951–2024) Chemometrician, spectroscopist and pioneer
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