Advancing eye disease detection: A comprehensive study on computer-aided diagnosis with vision transformers and SHAP explainability techniques

IF 6.6 2区 医学 Q1 ENGINEERING, BIOMEDICAL
Hossam Magdy Balaha , Asmaa El-Sayed Hassan , Rawan Ayman Ahmed , Magdy Hassan Balaha
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引用次数: 0

Abstract

Eye diseases such as age-related macular degeneration (AMD) and diabetic retinopathy are common worldwide and affect millions of people. These conditions can cause severe vision problems and even lead to blindness if not treated promptly. Therefore, accurate and timely diagnosis is crucial to manage these diseases effectively and prevent irreversible vision loss. This study introduces a computer-aided diagnosis (CAD) framework for automatically detecting various eye diseases via advanced methodologies and datasets. The main focus is on classifying fundus images, which is essential for precise diagnosis and prognosis. By incorporating cutting-edge techniques such as Vision Transformers (ViTs), this study aims to improve the performance and interpretability of traditional Convolutional Neural Networks (CNNs). ViTs can capture complex patterns and long-range dependencies in fundus images, helping distinguish between different eye diseases and healthy conditions. Furthermore, the study integrates SHapley additive exPlanations (SHAP) explainability techniques to provide insights into the model’s decision-making process, enhancing trust and understanding of its predictions. The results demonstrate significant performance enhancements compared with the baseline models, with an overall accuracy of 95%. This method outperforms previous state-of-the-art methods by a considerable margin. Additionally, metrics such as precision, recall, intersection over union (IoU), and the Matthews correlation coefficient (MCC) show superior performance across various eye diseases, such as diabetic retinopathy, glaucoma, and age-related macular degeneration. These findings underscore the effectiveness and reliability of the proposed approach in automated eye disease detection, indicating its potential for clinical integration and widespread adoption in healthcare settings.

Abstract Image

推进眼病检测:视觉变形和SHAP可解释性技术在计算机辅助诊断中的综合研究
诸如年龄相关性黄斑变性(AMD)和糖尿病性视网膜病变等眼病在世界范围内很常见,影响着数百万人。如果不及时治疗,这些情况会导致严重的视力问题,甚至导致失明。因此,准确、及时的诊断对于有效地控制这些疾病,防止不可逆的视力丧失至关重要。本研究介绍一种电脑辅助诊断(CAD)架构,透过先进的方法及资料,自动侦测各种眼疾。重点是对眼底图像进行分类,这对准确诊断和预后至关重要。通过结合视觉变换(ViTs)等前沿技术,本研究旨在提高传统卷积神经网络(cnn)的性能和可解释性。ViTs可以捕获眼底图像中的复杂模式和长期依赖关系,有助于区分不同的眼病和健康状况。此外,该研究整合了SHapley加性解释(SHAP)可解释性技术,以提供对模型决策过程的见解,增强对其预测的信任和理解。与基线模型相比,结果显示了显著的性能增强,总体准确率为95%。这种方法比以前最先进的方法要好得多。此外,精度、查全率、交叉超过联合(IoU)和马修斯相关系数(MCC)等指标在各种眼病(如糖尿病视网膜病变、青光眼和年龄相关性黄斑变性)中表现优异。这些发现强调了所提出的方法在自动眼病检测中的有效性和可靠性,表明其在临床整合和医疗保健机构中广泛采用的潜力。
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来源期刊
CiteScore
16.50
自引率
6.20%
发文量
77
审稿时长
38 days
期刊介绍: Biocybernetics and Biomedical Engineering is a quarterly journal, founded in 1981, devoted to publishing the results of original, innovative and creative research investigations in the field of Biocybernetics and biomedical engineering, which bridges mathematical, physical, chemical and engineering methods and technology to analyse physiological processes in living organisms as well as to develop methods, devices and systems used in biology and medicine, mainly in medical diagnosis, monitoring systems and therapy. The Journal''s mission is to advance scientific discovery into new or improved standards of care, and promotion a wide-ranging exchange between science and its application to humans.
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