Automatic diagnosis method for keratitis based on regional image patches from slit-lamp images
10.3969/j.issn.1005-202X.2025.09.015
- VernacularTitle:基于裂隙灯图像区域图像块的角膜炎自动诊断方法
- Author:
Jiewei JIANG
1
;
Ke DING
;
Yangyang FENG
;
Yu XIN
;
Jiamin GONG
;
Zhongwen LI
Author Information
1. 西安邮电大学电子工程学院,陕西 西安 710121
- Publication Type:Journal Article
- Keywords:
keratitis;
automatic diagnosis;
slit-lamp image patch;
feature fusion;
convolutional neural network;
cost-sensitive
- From:
Chinese Journal of Medical Physics
2025;42(9):1229-1235
- CountryChina
- Language:Chinese
-
Abstract:
A method that integrates the features of image patches from corneal lesions and conjunctival congestion-like complications is proposed to address the limitations of manual keratitis diagnosis(i.e.,time-consuming,laborious,high subjectivity)and the generally low accuracy of automatic keratitis diagnosis based on original slit-lamp images.Specifically,samples are acquired from the corneal and conjunctival regions.A cost-sensitive convolutional neural network is then used to extract and concatenate the high-level features of these image patches.After dimensionality reduction through principal component analysis,the processed features are input into the fully connected layer for classification.Trained and evaluated on 6414 slit-lamp images collected from Ningbo Eye Hospital,the proposed method achieves accuracies of 97.8%,98.6%,and 97.0%for keratitis,normal cornea,and other abnormal corneas,respectively.This method effectively integrates relevant features and provides a feasible solution for high-accuracy keratitis diagnosis.
