用深度学习提升糖尿病视网膜病变分级与异常检测准确率
Grading and Anomaly Detection for Automated Retinal Image Analysis using Deep Learning
- 融合多个预训练模型与自定义分类器，提升诊断性能
- 集成学习使分类准确率优于单一模型，特异性更高
- 适合医学影像分析、AI辅助诊断研究者参考
糖尿病视网膜病变（DR）是导致糖尿病患者失明的主要原因。本研究通过系统性文献回顾（PRISMA标准）分析了62篇相关论文，探讨深度学习在DR分级与病灶分割检测中的应用。研究聚焦基于CNN的模型及特征融合方法，评估数据增强与集成学习策略对分类准确率和鲁棒性的提升效果。结果表明，集成多个预训练网络与自定义分类器的方法显著优于单一模型，展现出高特异性。文章还综述了多种用于病灶检测的深度学习技术，强调持续研究的重要性，并指出其在个性化医疗与早期筛查中的潜力。
原文摘要 · Abstract (English)
The significant portion of diabetic patients was affected due to major blindness caused by Diabetic retinopathy (DR). For diabetic retinopathy, lesion segmentation, and detection the comprehensive examination is delved into the deep learning techniques application. The study conducted a systematic literature review using the PRISMA analysis and 62 articles has been investigated in the research. By including CNN-based models for DR grading, and feature fusion several deep-learning methodologies are explored during the study. For enhancing effectiveness in classification accuracy and robustness the data augmentation and ensemble learning strategies are scrutinized. By demonstrating the superior performance compared to individual models the efficacy of ensemble learning methods is investigated. The potential ensemble approaches in DR diagnosis are shown by the integration of multiple pre-trained networks with custom classifiers that yield high specificity. The diverse deep-learning techniques that are employed for detecting DR lesions are discussed within the diabetic retinopathy lesions segmentation and detection section. By emphasizing the requirement for continued research and integration into clinical practice deep learning shows promise for personalized healthcare and early detection of diabetics.
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