Automatic Diagnostic of the Presence of Exudates in Retinal Images Using Deep Learning
| Autor: | Joel J. P. C. Rodrigues, Deusimar Damião de Sousa, Ricardo A. L. Rabelo, Antonio Oseas de Carvalho Filho |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Retina
medicine.medical_specialty business.industry Deep learning Retinal Diabetic retinopathy 010501 environmental sciences medicine.disease 01 natural sciences 03 medical and health sciences chemistry.chemical_compound 0302 clinical medicine medicine.anatomical_structure chemistry Ophthalmology Diabetes mellitus Medicine Artificial intelligence Abnormality business Complication 030217 neurology & neurosurgery 0105 earth and related environmental sciences Retinopathy |
| Zdroj: | HealthCom |
| DOI: | 10.1109/healthcom49281.2021.9399000 |
| Popis: | Diabetes is one of the fastest-growing chronic diseases in the world. Diabetic retinopathy, a complication of Diabetes that affects vision, and if not treated promptly, can lead to total blindness of the patient. This abnormality has no cure, but if discovered in its early stages, there is a high chance that the patient will not reach total blindness. Detection of retinal background exudates is essential for the early diagnosis of diabetic retinopathy. In this paper, we present a deep learning model with a Convolutional Neural Network to diagnose exudates' presence or absence. The best results are about 99.52% sensitivity, 100% specificity, and about 99.76% accuracy for 1,608 images. Thus, the authors believe the proposed method can integrate a clinical system. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|