Deep learning in the grading of diabetic retinopathy: A review

Tajudin, Nurul Mirza Afiqah; Kipli, Kuryati; Mahmood, Muhammad Hamdi; Lim, Lik Thai; Mat, Dayang Azra Awang; Sapawi, Rohana; Sahari, Siti Kudnie; Lias, Kasumawati; Jali, Suriati Khartini; Hoque, Mohammed Enamul

doi:10.1049/cvi2.12116

Cited by 14 publications

(5 citation statements)

References 76 publications

(360 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multiple attempts are made to capture the image in order to minimize quality flaws like focusing issues, darkness, etc. [54] [55]. Ophthalmologists use a deep tissue imaging analysis to identify diabetic retinopathy.…”

Section: B Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Classification of Diabetic Retinopathy Images through Deep Learning Models - Color Channel Approach: A Review

Salih,

Abdulazeez

2024

ijcs

View full text Add to dashboard Cite

On a global scale diabetic retinopathy, or DR, is the most common cause of vision loss. Blindness can be prevented with prompt treatment and early identification with retinal screening. Automated analysis of fundus imagery is growing prominently as a means of increasing screening efficiency, thanks to the development of deep learning. This work focuses on deep learning methods for automatic DR severity grading using color channel information. First, we give some basic information on the etiology and color features of DR lesions. Next, a novel support for deep learning technique that use unprocessed color photos as input for comprehensive feature learning. A review is mentioned on color space encodings, data augmentation methods. A summary of the evaluation parameters and public databases that were utilized to benchmark DR techniques are provided. The objective of how color channel information in retinal pictures can be efficiently utilized by deep learning models for automated DR screening has been discussed with statistical support.

show abstract

Section: B Related Workmentioning

confidence: 99%

“…Diabetic diseases are caused by extremely unfavorable food choices and lifestyle alterations. Diabetes is more common in urban populations than in rural ones [6][7] [8].…”

Section: A Introductionmentioning

confidence: 99%

Classification of Diabetic Retinopathy Images through Deep Learning Models - Color Channel Approach: A Review

Salih,

Abdulazeez

2024

ijcs

View full text Add to dashboard Cite

show abstract

“…The original dataset's qualities that are thought to be the most informative are found in this sub-dataset. Figure 3 shows the suggested DR detection framework's layered concept [21].…”

Section: Selection Of Featuresmentioning

confidence: 99%

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

Sanamdikar,

Patil,

Patil

et al. 2023

ISI

View full text Add to dashboard Cite

Diabetic Retinopathy (DR), a microvascular complication, stands as one of the leading causes of vision impairment among diabetic populations globally. This pathology is characterized by the occlusion of retinal vessels, thereby depriving the retinal tissue of essential nutrients. Given the progressive nature of DR and its potential to culminate in irreversible blindness, timely and accurate diagnosis is paramount for effective intervention. Conventionally, the detection of DR relies heavily on the expertise of ophthalmologists, a resource-intensive process that may be prohibitive in terms of cost and time. To address these limitations, automated detection systems have been developed, aiming to hasten diagnostic processes and democratize access to these crucial services. Nevertheless, the performance of such systems has been historically hampered by the scarcity of reliable data sources and medical records for this condition. In response to these challenges, this study explores an ensemble machine learning approach that synergizes multiple established classifiers into a cohesive diagnostic model. The proposed methodology demonstrates superior performance in accuracy compared to prevalent classification algorithms. Utilizing the Messidor dataset, the top-performing five and ten features were isolated into four subdatasets through InfoGainEval and WrapperSubsetEval methods. The accuracy achieved for the top five features via InfoGainEval was 70.7%, while for the complete feature set, it reached 75.1%. The employment of ensemble machine learning techniques in diagnosing DR represents a significant application of artificial intelligence within the medical domain, conferring advantages such as enhanced accuracy, robustness, efficient feature selection, early detection, scalability, and a reduction in human error, all while ensuring costefficiency and enabling continuous monitoring for improved patient outcomes. However, the approach is not without limitations. These include the quantity and quality of data, clinical variability, ethical and privacy concerns, scalability challenges, potential overfitting, intricate feature selection and engineering, bias in data collection, and issues related to cost and accessibility. The findings underscore the efficacy of the sub-datasets, which facilitate a less cumbersome classification process as compared to the full Messidor dataset, thereby streamlining the diagnostic pathway.

show abstract

“…CNN is a Deep Learning technique where image features are extracted and classified by the model. Considerable amounts of research has been done in the domain of detecting Diabetic Retinopathy using deep learning [4,5].…”

Section: Introductionmentioning

confidence: 99%

DRNET: Improved Deep Learning Solution to detect Diabetic Retinopathy by incorporating Explainability, Uncertainty and Generalisation

Saxena

Narra

Saxena

2022

Preprint

View full text Add to dashboard Cite

Diabetic Retinopathy (DR) is the leading cause of blindness among adults and has no visible symptoms. Early detection is the key to prevent vision loss. Computer-Aided Diagnosis using Convolutional Neural Networks have recently gained momentum for DR detection as the cost can be significantly reduced while making the diagnosis more accessible. Even though these models give high accuracy, confidence in the diagnosis remains a mythical goal. In our opinion, the way to make the model’s diagnosis reliable is to enhance the trust in its predictions. In this work, we present a methodology to strengthen confidence in DR diagnosis using state of the art deep learning techniques by extending the pipeline with model uncertainty and explainability. Towards this goal, along with the model’s prediction we produce uncertainty scores and explainability maps to improve the users’ confidence in the diagnosis. Further, we attempt to generalise CNN models by exploring ensembling techniques such as bagging to improve performance of stand-alone models on external datasets i.e.-datasets other than the one that was used for training the model.

show abstract

Deep learning in the grading of diabetic retinopathy: A review

Cited by 14 publications

References 76 publications

Classification of Diabetic Retinopathy Images through Deep Learning Models - Color Channel Approach: A Review

Classification of Diabetic Retinopathy Images through Deep Learning Models - Color Channel Approach: A Review

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

DRNET: Improved Deep Learning Solution to detect Diabetic Retinopathy by incorporating Explainability, Uncertainty and Generalisation

Contact Info

Product

Resources

About