Deep Learning for Retinal Disease Detection Surveys | ||
| AL-Rafidain Journal of Computer Sciences and Mathematics | ||
| Article 1, Volume 16, Issue 2, December 2022, Pages 1-9 PDF (865.2 K) | ||
| Document Type: Review Paper | ||
| DOI: 10.33899/csmj.2022.176583 | ||
| Authors | ||
| attallh salih* 1; Manar Y. Kashmoola2 | ||
| 1جامعة الموصل کلیة علوم الحاسوب والریاضیات قسم علوم الحاسوب | ||
| 2Department of Computer Science College of Computer Science and Mathematics University of Mosul, Mosul, Iraq | ||
| Abstract | ||
| Retinal image analysis is crucial for The classification of retinal diseases such as “Age Related Macular Degeneration (AMD)”, “Diabetic Retinopathy (DR)”, “Retinoblastoma”, “Macular Bunker”, “Retinitis Pigmentosa”, and “Retinal Detachment”. The early detection of such diseases is important insofar as it contributes in mitigating future implications. Many approaches have been developed in the literature for auto-detecting of retinal landmarks and pathologies. The current revolution in deep learning techniques has opened the horizon for researchers in the field of ophthalmology. This paper is a comprehensive review of the deep learning techniques applied for the classification of retinal images, pathology, retinal landmarks, and disease classification. This review is based on indicators such as sensitivity, Area under ROC curve, specificity, F score, and accuracy. | ||
| Keywords | ||
| Machine learning; Deep learning; Retinal Disease; Disease Detection | ||
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