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Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey
| Content Provider | Scilit |
|---|---|
| Author | Asiri, Norah Hussain, Muhammad Adel, Fadwa Al Alzaidi, Nazih |
| Copyright Year | 2019 |
| Description | Journal: Artificial Intelligence in Medicine Diabetic retinopathy (DR) results in vision loss if not treated early. A computer-aided diagnosis (CAD) system based on retinal fundus images is an efficient and effective method for early DR diagnosis and assisting experts. A computer-aided diagnosis (CAD) system involves various stages like detection, segmentation and classification of lesions in fundus images. Many traditional machine-learning (ML) techniques based on hand-engineered features have been introduced. The recent emergence of deep learning (DL) and its decisive victory over traditional ML methods for various applications motivated the researchers to employ it for DR diagnosis, and many deep-learning-based methods have been introduced. In this paper, we review these methods, highlighting their pros and cons. In addition, we point out the challenges to be addressed in designing and learning about efficient, effective and robust deep-learning algorithms for various problems in DR diagnosis and draw attention to directions for future research. |
| Related Links | http://arxiv.org/pdf/1811.01238 |
| ISSN | 09333657 |
| e-ISSN | 18732860 |
| DOI | 10.1016/j.artmed.2019.07.009 |
| Journal | Artificial Intelligence in Medicine |
| Volume Number | 99 |
| Language | English |
| Publisher | Elsevier BV |
| Publisher Date | 2019-08-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Artificial Intelligence in Medicine Diabetic Retinopathy Diabetic Macular Edema |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Medicine |
