Affiliations: [a] Department of Computer Science and Technology, Manav Rachna University, Faridabad, Haryana, India
| [b] Department of Electronics & Communication Engineering, Noida Institute of Engineering and Technology, Greater Noida, U.P, India
| [c] Department of Computer Science and Engineering, North Cap University, Gurugram, Haryana, India
Correspondence:
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Corresponding author. Laxman Singh, Noida Institute of Engineering and Technology, Greater Noida, U.P, 201306, India. E-mail: [email protected].
Abstract: Diabetic Retinopathy (DR) is a disease that damages the retina of the human eye due to diabetic complications, resulting in a loss of vision. Blindness may be avoided If the DR disease is detected at an early stage. Unfortunately, DR is irreversible process, however, early detection and treatment of DR can significantly reduce the risk of vision loss. The manual diagnosis done by ophthalmologists on DR retina fundus images is time consuming, and error prone process. Nowadays, machine learning and deep learning have become one of the most effective approaches, which have even surpassed the human performance as well as performance of traditional image processing-based algorithms and other computer aided diagnosis systems in the analysis and classification of medical images. This paper addressed and evaluated the various recent state-of-the-art methodologies that have been used for detection and classification of Diabetic Retinopathy disease using machine learning and deep learning approaches in the past decade. Furthermore, this study also provides the authors observation and performance evaluation of available research using several parameters, such as accuracy, disease status, and sensitivity. Finally, we conclude with limitations, remedies, and future directions in DR detection. In addition, various challenging issues that need further study are also discussed.
