棉絮斑转诊自动化系统
Automated System for Referral of Cotton-Wool Spots.
作者信息
Naqvi Syed A G, Zafar Hafiz M F, Ul Haq Ihsan
机构信息
Department of Electronic Engineering, Faculty of Engineering and Technology, International Islamic University, Islamabad, Pakistan.
出版信息
Curr Diabetes Rev. 2018;14(2):168-174. doi: 10.2174/1573399812666161201114309.
BACKGROUND
Cotton-wool spots also referred as soft exudates are the early signs of complications in the eye fundus of the patients suffering from diabetic retinopathy. Early detection of exudates helps in the diagnosis of the disease and provides better medical attention.
METHODS
In this paper, an automated system for the detection of soft exudates has been suggested. The system has been developed by the combination of different techniques like Scale Invariant Feature Transform (SIFT), Visual Dictionaries, K-means clustering and Support Vector Machine (SVM).
RESULTS
The performance of the system is evaluated on a publically available dataset and AUC of 94.59% is achieved with the highest accuracy obtained is 94.59%. The experiments are also performed after mixing three datasets and AUC of 92.61% is observed with 91.94% accuracy.
CONCLUSION
The proposed system is easy to implement and can be used by medical experts both online and offline for referral of Cotton-wool spots in large populations. The system shows promising performance.
背景
棉絮斑也被称为软性渗出，是糖尿病视网膜病变患者眼底并发症的早期迹象。早期检测渗出有助于疾病诊断并提供更好的医疗护理。
方法
本文提出了一种用于检测软性渗出的自动化系统。该系统通过尺度不变特征变换（SIFT）、视觉词典、K均值聚类和支持向量机（SVM）等不同技术相结合开发而成。
结果
在一个公开可用的数据集上评估了该系统的性能，获得了94.59%的曲线下面积（AUC），最高准确率为94.59%。在混合三个数据集后也进行了实验，观察到AUC为92.61%，准确率为91.94%。
结论
所提出的系统易于实施，医学专家可在线和离线使用，用于在大量人群中转诊棉絮斑。该系统表现出良好的性能。
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