Detection of diabetic retinopathy and related retinal disorders using fundus images based on deep learning and image processing techniques: A comprehensive review

Abstract

Diabetes mellitus is a chronic disorder disease in which a person's body fails to adhere insulin produced by their pancreas or unable to segregate enough insulin due to harmonic imbalance. Diabetic people are suffering from eye disorders like diabetic retinopathy (DR), glaucoma and various diseases such as neuropathy, nephropathy, cardiomyopathy over long intervals. One of the most prevalent diabetic consequence is DR. Detecting the morphological variations in retina is difficult and requires an effective automated detection system. DR can be predicted in earlier stage using tremendous development of deep learning models and image processing techniques. Recently, many research articles have been published in DR diagnosis system. This article shows a comprehensive review of automated diagnostic methods for DR detection and other related eye disorders from several points: Causes for DR, publicly available datasets, image preprocessing, segmentation of various DR lesions, feature optimization, various deep learning models, and open research challenges. The study offers a thorough overview of DR detection techniques, which delivers valuable information for researchers, medical professionals, and DR affected patients.

Is it time to consider teleophthalmology as a game-changer in the management of diabetic retinopathy?

“…The general consensus is that teleophthalmology based on AI is a viable promising approach to detect DR, (51,53,55,56,60,(62)(63)(64)(65)(66)(67)(68)(69) and also cost-effective. (70)(71)(72)(73)(74) From the literature, it is observed that a few researchers have carried out a review on smartphone-based retinal image analysis concluding that it is a quick and cost-effective tool.…”

“…Recent reviews and surveys as those of authors like Fenner et al, (47) Ting et al, (48) Asiri et al, (49) Grzybowski et al, (50) Stolte et al, (51) He et al, (52) Bilal et al, (53) Williamson, (54) Lalithadevi et al, (55) Iqbal et al, (56) Celard et al (57) and Vujosevic et al (58) cover a significant amount of works that apply DL to retinal image analysis in order to diagnose DR. A metaanalysis conducted by Wu and coworkers in 2019 (59) and 2021 (60) evidenced a high efficacy of ML algorithms in DR detection, specifically mtmDR. The authors concluded that "ML-based DR screening algorithms are with capability of detecting early DR. Further research directed to detect retinal vessels were continued by Jiang et al (89) and Arsalan et al (90) .…”

Enhanced Detection of Diabetic Retinopathy Using Ensemble Machine Learning: A Comparative Study

“…The researchers solely utilised CLAHE to enhance colour contrast, a method used to identify lesions associated to vessels from lesions unrelated to vessels. For this diagnostic model, the pre-trained CNNs family, Efficient Nets, was shown to be the most helpful in order to increase the model's accuracy in comparison to Lalithadevi and Krishnaveni [17]. Additionally, demonstrated to be far more precise and timeefficient are pre-trained CNNs.…”

Effectiveness of educational intervention on self-care practices of critically ill patients with diabetic retinopathy

“…The International Diabetes Federation (IDF) estimated the total number of people living with diabetes is projected to rise to 643 million by 2030 and 783 million by 2045 (The International Diabetes Federation (IDF), 2021). Diabetic retinopathy develops when uncontrolled blood glucose level damages the tiny blood vessels in the eye retina causing a variety of symptoms that varies from mild visual disturbance and can reach irreversible vision loss (Lalithadevi & Krishnaveni, 2022). In 2020, the worldwide number of adults with Diabetic retinopathy, VTDR, and CSME was estimated to be 103.12 million, 28.54 million, and 18.83 million, respectively; by 2045, the numbers are estimated to increase to reach 160.50 million, 44.82 million, and 28.61 million, respectively.…”