A critical review on diagnosis of diabetic retinopathy using machine learning and deep learning

• Published in: Multimedia tools and applications Vol. 81; no. 18; pp. 25613 - 25655
• Main Authors: Das, Dolly, Biswas, Saroj Kr, Bandyopadhyay, Sivaji
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2022; Springer Nature B.V
• Subjects: Blindness; Computer Communication Networks; Computer Science; Data processing; Data Structures and Information Theory; Datasets; Deep learning; Diabetes; Diabetes mellitus; Diabetic retinopathy; Diagnosis; Feature extraction; Image classification; Image enhancement; Machine learning; Multimedia Information Systems; Special Purpose and Application-Based Systems; Deep learning; Diabetic retinopathy; Feature extraction; Image processing; Machine learning; Retinal lesions
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-022-12642-4

Diabetic Retinopathy (DR) is a health condition caused due to Diabetes Mellitus (DM). It causes vision problems and blindness due to disfigurement of human retina. According to statistics, 80% of diabetes patients battling from long diabetic period of 15 to 20 years, suffer from DR. Hence, it has become a dangerous threat to the health and life of people. To overcome DR, manual diagnosis of the disease is feasible but overwhelming and cumbersome at the same time and hence requires a revolutionary method. Thus, such a health condition necessitates primary recognition and diagnosis to prevent DR from developing into severe stages and prevent blindness. Innumerable Machine Learning (ML) models are proposed by researchers across the globe, to achieve this purpose. Various feature extraction techniques are proposed for extraction of DR features for early detection. However, traditional ML models have shown either meagre generalization throughout feature extraction and classification for deploying smaller datasets or consumes more of training time causing inefficiency in prediction while using larger datasets. Hence Deep Learning (DL), a new domain of ML, is introduced. DL models can handle a smaller dataset with help of efficient data processing techniques. However, they generally incorporate larger datasets for their deep architectures to enhance performance in feature extraction and image classification. This paper gives a detailed review on DR, its features, causes, ML models, state-of-the-art DL models, challenges, comparisons and future directions, for early detection of DR.