Purpose: We aim to present effective diagnostics in the field of ophthalmology and improve eye health. The purpose of this study is to examine the capability of health classification of Meibomian gland dysfunction (MGD) using Keratography 5M and AlexNet method.
Method: A total of 4,609 meibomian gland images were collected from 2,000 patients using Keratography 5M in the hospital. Then, MGD dataset for eyelid gland health recognition was constructed through image pre-processing, labelling, cropping and augmentation. Furthermore, AlexNet network was used to identify the eyelid gland health. The effects of different optimization methods, different learning rates, Dropout methods and different batch sizes on the recognition accuracy were discussed.
Results: The results show that the effect of model recognition is the best when the optimized method is Adam, the number of iterations is 150, the learning rate is 0.001, and the batch size is 80, then, the overall test accuracy of health degree is 94.00%.
Conclusion: Our research provides a reference to the clinical diagnosis or screening of eyelid gland dysfunction. In future implementations, ophthalmologists can implement more advanced learning algorithms to improve the accuracy of diagnosis.
Keywords: AlexNet; Deep learning; Keratography 5M; MGD dataset; Meibomian gland dysfunction; Ophthalmology.
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