.png){kind=icon}
返回
Effective automatic classification methods via deep learning for myopic maculopathy
.png){kind=icon}
OA
AI
DOI：10.3389/fmed.2024.1492808.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Background Pathologic myopia (PM) associated with myopic maculopathy (MM) is a significant cause of visual impairment, especially in East Asia, where its prevalence has surged. Early detection and accurate classification of myopia-related fundus lesions are critical for managing PM. Traditional clinical analysis of fundus images is time-consuming and dependent on specialist expertise, driving the need for automated, accurate diagnostic tools.Methods This study developed a deep learning-based system for classifying five types of MM using color fundus photographs. Five architectures-ResNet50, EfficientNet-B0, Vision Transformer (ViT), Contrastive Language-Image Pre-Training (CLIP), and RETFound-were utilized. An ensemble learning approach with weighted voting was employed to enhance model performance. The models were trained on a dataset of 2,159 annotated images from Shenzhen Eye Hospital, with performance evaluated using accuracy, sensitivity, specificity, F1-Score, Cohen's Kappa, and area under the receiver operating characteristic curve (AUC).Results The ensemble model achieved superior performance across all metrics, with an accuracy of 95.4% (95% CI: 93.0-97.0%), sensitivity of 95.4% (95% CI: 86.8-97.5%), specificity of 98.9% (95% CI: 97.1-99.5%), F1-Score of 95.3% (95% CI: 93.2-97.2%), Kappa value of 0.976 (95% CI: 0.957-0.989), and AUC of 0.995 (95% CI: 0.992-0.998). The voting ensemble method demonstrated robustness and high generalization ability in classifying complex lesions, outperforming individual models.Conclusion The ensemble deep learning system significantly enhances the accuracy and reliability of MM classification. This system holds potential for assisting ophthalmologists in early detection and precise diagnosis, thereby improving patient outcomes. Future work could focus on expanding the dataset, incorporating image quality assessment, and optimizing the ensemble algorithm for better efficiency and broader applicability.
Keyword:
myopic maculapathy
ensemble learning
deep learning
artificial intelligence
fundus image
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
F
IF:
3
论文数:
2.2W
被引数:
4.0W
机构
引用论文
分享收藏Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks使用深度神经网络自动检测视网膜照片中的39种眼底疾病和状况.png)
NATURE COMMUNICATIONSOAAI
NATURE COMMUNICATIONSIF15.7
OAAI分享收藏Early Cenozoic topography, morphology, and tectonics of the northern Sierra Nevada and western Basin and RangeGeosphereOAAI
GeosphereIF0
OAAI分享收藏分享收藏Do Perceptions of Competence Mediate The Relationship Between Fundamental Motor Skill Proficiency and Physical Activity Levels of Children in Kindergarten?能力的感知是否可以介导幼儿园儿童的基本运动技能熟练程度与身体活动水平之间的关系？
PREAI
分享收藏Research on the Efficiency of Wireless Power Transfer System Based on Multi-Auxiliary Transmitting Coils基于多辅助发射线圈的无线电能传输系统效率研究
PREAI
分享收藏
查看更多内容