ArticleScientific reports2017
Comparative analysis of image classification methods for automatic diagnosis of ophthalmic images.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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22 citing papers in PubMed, 59 citations in OpenAlex.
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- Automatic classification of heterogeneous slit-illumination images using an ensemble of cost-sensitive convolutional neural networks.Annals of translational medicine · 2021Article
- Improving the Generalizability of Infantile Cataracts Detection via Deep Learning-Based Lens Partition Strategy and Multicenter Datasets.Frontiers in medicine · 2021Article
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- Application of artificial intelligence in anterior segment ophthalmic diseases: diversity and standardization.Annals of translational medicine · 2020Review
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- Different Machine Learning and Deep Learning Methods for the Classification of Colorectal Cancer Lymph Node Metastasis Images.Frontiers in bioengineering and biotechnology · 2020Article
- Application of machine learning in ophthalmic imaging modalities.Eye and vision (London, England) · 2020Review
- Prediction of postoperative complications of pediatric cataract patients using data mining.Journal of translational medicine · 2019Article
- Development and validation of deep learning algorithms for scoliosis screening using back images.Communications biology · 2019Article
- Predicting the progression of ophthalmic disease based on slit-lamp images using a deep temporal sequence network.PloS one · 2018Article
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Authors and funding
17 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There are many image classification methods, but it remains unclear which methods are most helpful for analyzing and intelligently identifying ophthalmic images. We select representative slit-lamp images which show the complexity of ocular images as research material to compare image classification algorithms for diagnosing ophthalmic diseases. To facilitate this study, some feature extraction algorithms and classifiers are combined to automatic diagnose pediatric cataract with same dataset and then their performance are compared using multiple criteria. This comparative study reveals the general characteristics of the existing methods for automatic identification of ophthalmic images and provides new insights into the strengths and shortcomings of these methods. The relevant methods (local binary pattern +SVMs, wavelet transformation +SVMs) which achieve an average accuracy of 87% and can be adopted in specific situations to aid doctors in preliminarily disease screening. Furthermore, some methods requiring fewer computational resources and less time could be applied in remote places or mobile devices to assist individuals in understanding the condition of their body. In addition, it would be helpful to accelerate the development of innovative approaches and to apply these methods to assist doctors in diagnosing ophthalmic disease.
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