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Clinical Ophthalmology: A Systematic Approach
TLDR
Ocular side-effects of systemic medication 21.1.Abstract:
1. Eyelids 2. Lacrimal Drainage System 3. Orbit 4. Dry Eye Disorders 5. Conjunctiva 6. Cornea 7. Corneal and Refractive Surgery 8. Episclera and Sclera 9. Lens 10. Glaucoma 11. Uveitis 12. Ocular Tumours 13. Retinal Vascular Disease 14. Acquired Macular Disorders 15. Hereditary Fundus Dystrophies 16. Retinal Detachment 17. Vitreous Opacities 18. Strabismus 19. Neuro-ophthalmology 20. Ocular side-effects of systemic medication 21. Trauma Indexread more
Citations
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Journal ArticleDOI
Accurate Retinal Vessel Segmentation in Color Fundus Images via Fully Attention-Based Networks
TL;DR: A novel Fully Attention-based Network (FANet) based on attention mechanisms to adaptively learn rich feature representation and aggregate the multi-scale information and demonstrates that the proposed model can effectively identify irregular, noisy, and multi- scale retinal vessels.
Journal ArticleDOI
Comparative evaluation of intraocular pressure with an air-puff tonometer versus a Goldmann applanation tonometer
TL;DR: Because measurements of IOP by AP tonometer are usually higher than those obtained by GAT regardless of the patient’s age, sex, or laterality of eyes, AP tonometry is a suitable method for community or mass screenings of Iop.
Proceedings ArticleDOI
Detection of Hard Exudates in Retinal Fundus Images Using Deep Learning
TL;DR: In this paper, a deep learning algorithm has been presented in this paper that detects hard exudates in fundus images of the retina, which is one of the main reasons for the preventable blindness all over the world.
Journal ArticleDOI
A novel retinal vessel detection approach based on multiple deep convolution neural networks.
TL;DR: This paper proposes a multiple classifier framework based on deep convolutional neural networks (MDCNN) that achieves better performance and significantly outperforms the state-of-the-art for automatic retinal vessel segmentation on the DRIVE dataset.