Detecting Diabetic Retinopathy Using Embedded Computer Vision

Vora, Parshva; Shrestha, Sudhir

doi:10.3390/app10207274

Cited by 20 publications

(9 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The information must originate from reputable organizations and be labelled correctly. EyePacs donated the Kaggle database that we utilized [ 19 ]. Greater than 1000 people were examined, and retinal photographs were captured by EyePacs.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A Deep Learning Framework for Earlier Prediction of Diabetic Retinopathy from Fundus Photographs

Gunasekaran

Pitchai²,

Chaitanya

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

Diabetic patients can also be identified immediately utilizing retinopathy photos, but it is a challenging task. The blood veins visible in fundus photographs are used in several disease diagnosis approaches. We sought to replicate the findings published in implementation and verification of a deep learning approach for diabetic retinopathy identification in retinal fundus pictures. To address this issue, the suggested investigative study uses recurrent neural networks (RNN) to retrieve characteristics from deep networks. As a result, using computational approaches to identify certain disorders automatically might be a fantastic solution. We developed and tested several iterations of a deep learning framework to forecast the progression of diabetic retinopathy in diabetic individuals who have undergone teleretinal diabetic retinopathy assessment in a basic healthcare environment. A collection of one-field or three-field colour fundus pictures served as the input for both iterations. Utilizing the proposed DRNN methodology, advanced identification of the diabetic state was performed utilizing HE detected in an eye’s blood vessel. This research demonstrates the difficulties in duplicating deep learning approach findings, as well as the necessity for more reproduction and replication research to verify deep learning techniques, particularly in the field of healthcare picture processing. This development investigates the utilization of several other Deep Neural Network Frameworks on photographs from the dataset after they have been treated to suitable image computation methods such as local average colour subtraction to assist in highlighting the germane characteristics from a fundoscopy, thus, also enhancing the identification and assessment procedure of diabetic retinopathy and serving as a skilled guidelines framework for practitioners all over the globe.

show abstract

Section: Resultsmentioning

confidence: 99%

“…An extended eye test is currently used to identify diabetic retinopathy. Eye dilating drops are injected into a patient's eye to enlarge the pupil but also enable doctors to visualize the blood vessels in the eyes [ 19 ]. A particular dye is inserted, and images of the dye as it flows through blood vessels are obtained.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Framework for Earlier Prediction of Diabetic Retinopathy from Fundus Photographs

Gunasekaran

Pitchai²,

Chaitanya

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

show abstract

“… In contrast to MA, HM are characterized by big spots on the retina with uneven edge widths of more than 125 μm. A hemorrhage can be either flame or blot, according to whether the spots are on the surface or deeper in the tissue [ 12 , 13 ]. The swelling of nerve fibers causes soft exudates, which appear as white ovals on the retina as defined as SX [ 1 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Prediction of Diabetic Retinopathy Using CLAHE and ESRGAN for Enhancement

2023

View full text Add to dashboard Cite

Vision loss can be avoided if diabetic retinopathy (DR) is diagnosed and treated promptly. The main five DR stages are none, moderate, mild, proliferate, and severe. In this study, a deep learning (DL) model is presented that diagnoses all five stages of DR with more accuracy than previous methods. The suggested method presents two scenarios: case 1 with image enhancement using a contrast limited adaptive histogram equalization (CLAHE) filtering algorithm in conjunction with an enhanced super-resolution generative adversarial network (ESRGAN), and case 2 without image enhancement. Augmentation techniques were then performed to generate a balanced dataset utilizing the same parameters for both cases. Using Inception-V3 applied to the Asia Pacific Tele-Ophthalmology Society (APTOS) datasets, the developed model achieved an accuracy of 98.7% for case 1 and 80.87% for case 2, which is greater than existing methods for detecting the five stages of DR. It was demonstrated that using CLAHE and ESRGAN improves a model’s performance and learning ability.

show abstract

“…Diabetic Retinopathy detection involves identifying 5 stages which are, no DR, mild DR, moderate DR, severe DR and proliferative DR [4], [8]. Figure 1 [7] illustrates the five possible stages of DR development.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Techniques for Diabetic Retinopathy Classification: A Survey

2022

View full text Add to dashboard Cite

Diabetic Retinopathy (DR) is a degenerative disease that impacts the eyes and is a consequence of Diabetes mellitus, where high blood glucose levels induce lesions on the eye retina. Diabetic Retinopathy is regarded as the leading cause of blindness for diabetic patients, especially the working-age population in developing nations. Treatment involves sustaining the patient's current grade of vision since the disease is irreversible. Early detection of Diabetic Retinopathy is crucial in order to sustain the patient's vision effectively. The main issue involved with DR detection is that the manual diagnosis process is very time, money and effort consuming, and involves an ophthalmologist's examination of eye retinal fundus images. The latter also proves to be more difficult, particularly in the early stages of the disease when disease features are less prominent in the images. Machine learning-based medical image analysis has proven competency in assessing retinal fundus images and the utilization of deep learning algorithms has aided the early diagnosis of Diabetic Retinopathy (DR). This paper reviews and analyzes state-of-the-art deep learning methods in supervised, self-supervised, Vision Transformer etc. setups, proposing retinal fundus image classification and detection. For instance, referable, non-referable, and proliferative classification of Diabetic Retinopathy are reviewed and summarized. Moreover, the paper discusses available retinal fundus datasets for Diabetic Retinopathy that are used for tasks such as detection, classification and segmentation. The paper also assesses research gaps in the area of DR detection/classification and addresses various challenges that need further study and investigation.

show abstract

Detecting Diabetic Retinopathy Using Embedded Computer Vision

Cited by 20 publications

References 14 publications

A Deep Learning Framework for Earlier Prediction of Diabetic Retinopathy from Fundus Photographs

A Deep Learning Framework for Earlier Prediction of Diabetic Retinopathy from Fundus Photographs

Deep Learning-Based Prediction of Diabetic Retinopathy Using CLAHE and ESRGAN for Enhancement

Deep Learning Techniques for Diabetic Retinopathy Classification: A Survey

Contact Info

Product

Resources

About