A Comparison of Computer Vision Methods for the Combined Detection of Glaucoma, Diabetic Retinopathy and Cataracts

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12722)


This paper focuses on the accurate, combined detection of glaucoma, diabetic retinopathy, and cataracts, all using a single computer vision pipeline. Attempts have been made in past literature; however, they mainly focus on only one of the aforementioned eye diseases. These diseases must be identified in the early stages to prevent damage progression. Three pipelines were constructed, of which 12 deep learning models and 8 Support Vector Machines (SVM) classifiers were trained. Pipeline 1 extracted Histogram of Oriented Gradients (HOG) features, and pipeline 2 extracted Grey-Level Co-occurrence Matrix (GLCM) textural features from the pre-processed images. These features were classified with either a linear or Radial Basis Function (RBF) kernel SVM. Pipeline 3 utilised various deep learning architectures for feature extraction and classification. Two models were trained for each deep learning architecture and SVM classifier, using standard RGB images (labelled as Normal). The other uses retina images with only the green channel present (labelled as Green). The Inception V3 Normal model achieved the best performance with accuracy and an F1-Score of 99.39%. The SqueezeNet Green model was the worst-performing deep learning model with accuracy and an F1-Score of 81.36% and 81.29%, respectively. Although it performed the worst, the model size is 5.03 MB compared to the 225 MB model size of the top-performing Inception V3 model. A GLCM feature selection study was performed for both the linear and RBF SVM kernels. The RBF SVM that extracted HOG features on the green-channel images performed the best out of the SVMs with accuracy and F1-Score of 76.67% and 76.48%, respectively. The green-channel extraction was more effective on the SVM classifiers than the deep learning models. The Inception V3 Normal model can be integrated with a computer-aided system to facilitate examiners in detecting diabetic retinopathy, cataracts and glaucoma.


Glaucoma Diabetic retinopathy Cataract Computer vision Convolutional neural network Deep learning GLCM HOG CAD 


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© Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.University of JohannesburgJohannesburgSouth Africa

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