A review on computer-aided recent developments for automatic detection of diabetic retinopathy
Autor: | Santosh Nagnath Randive, Amol D. Rahulkar, Ranjan K. Senapati |
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Rok vydání: | 2019 |
Předmět: |
0206 medical engineering
Biomedical Engineering Hemorrhage 02 engineering and technology Fundus (eye) 01 natural sciences Machine Learning Image Interpretation Computer-Assisted medicine Animals Humans In real life Retinal blood vessels Diabetic Retinopathy Blindness business.industry 010401 analytical chemistry General Medicine Diabetic retinopathy Microaneurysm medicine.disease 020601 biomedical engineering 0104 chemical sciences Computer-aided Optometry business Algorithms Retinopathy |
Zdroj: | Journal of Medical Engineering & Technology. 43:87-99 |
ISSN: | 1464-522X 0309-1902 |
DOI: | 10.1080/03091902.2019.1576790 |
Popis: | Diabetic retinopathy is a serious microvascular disorder that might result in loss of vision and blindness. It seriously damages the retinal blood vessels and reduces the light-sensitive inner layer of the eye. Due to the manual inspection of retinal fundus images on diabetic retinopathy to detect the morphological abnormalities in Microaneurysms (MAs), Exudates (EXs), Haemorrhages (HMs), and Inter retinal microvascular abnormalities (IRMA) is very difficult and time consuming process. In order to avoid this, the regular follow-up screening process, and early automatic Diabetic Retinopathy detection are necessary. This paper discusses various methods of analysing automatic retinopathy detection and classification of different grading based on the severity levels. In addition, retinal blood vessel detection techniques are also discussed for the ultimate detection and diagnostic procedure of proliferative diabetic retinopathy. Furthermore, the paper elaborately discussed the systematic review accessed by authors on various publicly available databases collected from different medical sources. In the survey, meta-analysis of several methods for diabetic feature extraction, segmentation and various types of classifiers have been used to evaluate the system performance metrics for the diagnosis of DR. This survey will be helpful for the technical persons and researchers who want to focus on enhancing the diagnosis of a system that would be more powerful in real life. |
Databáze: | OpenAIRE |
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