TY - GEN
T1 - A Comparison of Computer Vision Methods for the Combined Detection of Glaucoma, Diabetic Retinopathy and Cataracts
AU - Orfao, Jarred
AU - van der Haar, Dustin
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - CAD
KW - Cataract
KW - Computer vision
KW - Convolutional neural network
KW - Deep learning
KW - Diabetic retinopathy
KW - GLCM
KW - Glaucoma
KW - HOG
UR - http://www.scopus.com/inward/record.url?scp=85112200242&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-80432-9_3
DO - 10.1007/978-3-030-80432-9_3
M3 - Conference contribution
AN - SCOPUS:85112200242
SN - 9783030804312
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 30
EP - 42
BT - Medical Image Understanding and Analysis - 25th Annual Conference, MIUA 2021, Proceedings
A2 - Papież, Bartłomiej W.
A2 - Yaqub, Mohammad
A2 - Jiao, Jianbo
A2 - Namburete, Ana I.
A2 - Noble, J. Alison
PB - Springer Science and Business Media Deutschland GmbH
T2 - 25th Annual Conference on Medical Image Understanding and Analysis, MIUA 2021
Y2 - 12 July 2021 through 14 July 2021
ER -