Automatic Cataract Classification Using Deep Neural Network With Discrete State Transition

Cataract is the clouding of lens, which affects vision and it is the leading cause of blindness in the world's population. Accurate and convenient cataract detection and cataract severity evaluation will improve the situation. Automatic cataract detection and grading methods are proposed in thi...

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Bibliographic Details
Published in:IEEE transactions on medical imaging 2020-02, Vol.39 (2), p.436-446
Main Authors: Zhou, Yue, Li, Guoqi, Li, Huiqi
Format: Article
Language:English
Subjects:
Adult
Aged
Aged, 80 and over
Algorithms
Artificial neural networks
Biological neural networks
Blindness
Cataract - diagnostic imaging
Cataract classification
Cataracts
Classification
Diagnostic Techniques, Ophthalmological
discrete ResNet
exponential discrete state transition (EDST)
Feature extraction
Humans
Image classification
Image Interpretation, Computer-Assisted - methods
Image processing
Knowledge engineering
Medical imaging
Middle Aged
Multilayer perceptrons
Neural networks
Neural Networks, Computer
Retina
Retina - diagnostic imaging
retinal image
Training
Wavelet Analysis
Young Adult
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Summary:Cataract is the clouding of lens, which affects vision and it is the leading cause of blindness in the world's population. Accurate and convenient cataract detection and cataract severity evaluation will improve the situation. Automatic cataract detection and grading methods are proposed in this paper. With prior knowledge, the improved Haar features and visible structure features are combined as features, and multilayer perceptron with discrete state transition (DST-MLP) or exponential DST (EDST-MLP) are designed as classifiers. Without prior knowledge, residual neural networks with DST (DST-ResNet) or EDST (EDST-ResNet) are proposed. Whether with prior knowledge or not, our proposed DST and EDST strategy can prevent overfitting and reduce storage memory during network training and implementation, and neural networks with these strategies achieve state-of-the-art accuracy in cataract detection and grading. The experimental results indicate that combined features always achieve better performance than a single type of feature, and classification methods with feature extraction based on prior knowledge are more suitable for complicated medical image classification task. These analyses can provide constructive advice for other medical image processing applications.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2019.2928229