A Deep Learning Based Diabetic Retinopathy Detection from Retinal Images

Yadav, M Narsing; Goel, Raghav; Rajeswari, D

doi:10.1109/conit51480.2021.9498502

Cited by 19 publications

(3 citation statements)

References 6 publications

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“…Table 1 summarizes the many attempts to detect DR anomalies in photos using various DL techniques [ 19 , 24 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ]. According to the results of the research into DR identification and diagnostic methods, there are still a lot of loopholes that need to be investigated.…”

Section: Related Workmentioning

confidence: 99%

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

2023

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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.

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Section: Related Workmentioning

confidence: 99%

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

2023

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“…Traditional image processing and DL both have their place in such picture categorization challenges. 9 , 39 – 44 Investigations on DR identification and diagnostic approaches revealed certain gaps that must be reevaluated. For instance, due to a lack of relevant data, insufficient work has been devoted to developing and training a distinctive DL model.…”

Section: Related Workmentioning

confidence: 99%

Deep learning-enhanced diabetic retinopathy image classification

et al. 2023

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Objective Diabetic retinopathy (DR) can sometimes be treated and prevented from causing irreversible vision loss if caught and treated properly. In this work, a deep learning (DL) model is employed to accurately identify all five stages of DR. Methods The suggested methodology presents two examples, one with and one without picture augmentation. A balanced dataset meeting the same criteria in both cases is then generated using augmentative methods. The DenseNet-121-rendered model on the Asia Pacific Tele-Ophthalmology Society (APTOS) and dataset for diabetic retinopathy (DDR) datasets performed exceptionally well when compared to other methods for identifying the five stages of DR. Results Our propose model achieved the highest test accuracy of 98.36%, top-2 accuracy of 100%, and top-3 accuracy of 100% for the APTOS dataset, and the highest test accuracy of 79.67%, top-2 accuracy of 92.%76, and top-3 accuracy of 98.94% for the DDR dataset. Additional criteria (precision, recall, and F1-score) for gauging the efficacy of the proposed model were established with the help of APTOS and DDR. Conclusions It was discovered that feeding a model with higher-quality photographs increased its efficiency and ability for learning, as opposed to both state-of-the-art technology and the other, non-enhanced model.

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“…A computer vision-based method to analyse and forecast diabetes from input retinal images was proposed by Mini Yadav et al [19]. This facilitates the early identification of DR. Pre-processing, feature extraction, and segmentation techniques are used in the aforementioned image processing stage.…”

Section: Related Work IImentioning

confidence: 99%

Enhancing Diabetic Retinopathy Detection Through Machine Learning with Restricted Boltzmann Machines

Naramala,

Kumar,

Rao

et al. 2023

IJACSA

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Diabetes is a potentially sight-threatening condition that can lead to blindness if left undetected. Timely diagnosis of diabetic retinopathy, a persistent eye ailment, is critical to prevent irreversible vision loss. However, the traditional method of diagnosing diabetic retinopathy through retinal testing by ophthalmologists is labor-intensive and time-consuming. Additionally, early identification of glaucoma, indicated by the Cup-to-Disc Ratio (CDR), is vital to prevent vision impairment, yet its subtle initial symptoms make timely detection challenging. This research addresses these diagnostic challenges by leveraging machine learning and deep learning techniques. In particular, the study introduces the application of Restricted Boltzmann Machines (RBM) to the domain. By extracting and analyzing multiple features from retinal images, the proposed model aims to accurately categorize anomalies and automate the diagnostic process. The investigation further advances with the utilization of a U-network model for optic segmentation and employs the Squirrel Search Algorithm (SSA) to fine-tune RBM hyperparameters for optimal performance. The experimental evaluation conducted on the RIM-ONE DL dataset demonstrates the efficacy of the proposed methodology. A comprehensive comparison of results against previous prediction models is carried out, assessing accuracy, cross-validation, and Receiver Operating Characteristic (ROC) metrics. Remarkably, the proposed model achieves an accuracy value of 99.2% on the RIM-ONE DL dataset. By bridging the gap between automated diagnosis and ophthalmological practice, this research contributes significantly to the medical field. The model's robust performance and superior accuracy offer a promising avenue to support healthcare professionals in enhancing their decisionmaking processes, ultimately improving the quality of care for patients with retinal anomalies.

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A Deep Learning Based Diabetic Retinopathy Detection from Retinal Images

Cited by 19 publications

References 6 publications

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

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

Deep learning-enhanced diabetic retinopathy image classification

Enhancing Diabetic Retinopathy Detection Through Machine Learning with Restricted Boltzmann Machines

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