Diabetic Retinopathy Detection by means of Deep Learning

Thorat, Sumit; Chavan, Akshay; Sawant, Pratik; Kulkarni, Sharvika; Sisodiya, Nitin; Kolapkar, Anand

doi:10.1109/iciccs51141.2021.9432075

Cited by 8 publications

(3 citation statements)

References 9 publications

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“…The DL technique detects DR, a complex disease caused by high glucose levels that affect the optical nerve. The model, trained on 35126 retinal images, achieved an accuracy of 81% ( Chaudhary & Ramya, 2020 ; Thorat et al, 2021 ) Automatic detection systems for DR are being developed to speed up and reduce costs. However, the accuracy of these systems is unsatisfactory due to the scarcity of reliable data.…”

Section: Machine Learning In Diabetic Retinopathymentioning

confidence: 99%

A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images

Bhulakshmi,

Rajput

2024

PeerJ Computer Science

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Diabetic retinopathy (DR) is the leading cause of visual impairment globally. It occurs due to long-term diabetes with fluctuating blood glucose levels. It has become a significant concern for people in the working age group as it can lead to vision loss in the future. Manual examination of fundus images is time-consuming and requires much effort and expertise to determine the severity of the retinopathy. To diagnose and evaluate the disease, deep learning-based technologies have been used, which analyze blood vessels, microaneurysms, exudates, macula, optic discs, and hemorrhages also used for initial detection and grading of DR. This study examines the fundamentals of diabetes, its prevalence, complications, and treatment strategies that use artificial intelligence methods such as machine learning (ML), deep learning (DL), and federated learning (FL). The research covers future studies, performance assessments, biomarkers, screening methods, and current datasets. Various neural network designs, including recurrent neural networks (RNNs), generative adversarial networks (GANs), and applications of ML, DL, and FL in the processing of fundus images, such as convolutional neural networks (CNNs) and their variations, are thoroughly examined. The potential research methods, such as developing DL models and incorporating heterogeneous data sources, are also outlined. Finally, the challenges and future directions of this research are discussed.

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Section: Machine Learning In Diabetic Retinopathymentioning

confidence: 99%

A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images

Bhulakshmi,

Rajput

2024

PeerJ Computer Science

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“…The testing of this pipeline's efficiency was done against several mainstream CNN models and achieved a accuracy of 88.72%. Sumit Thorat and Akshay Chavan [9]…”

Section: Related Workmentioning

confidence: 99%

Diabetic Retinopathy Detection From Fundus Images Using Multi-Tasking Model With EfficientNet B5

Bhawarkar¹,

Bhure²,

Chaudhary³

et al. 2022

ITM Web Conf.

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Diabetic Retinopathy (DR) is a common eye disease that affects over 3 million people annually. People with diabetes are more prone to suffer from Diabetic Retinopathy. This condition can cause blurring of vision and blindness. Early detection and treatment are the most effective ways to manage Diabetic Retinopathy. Due to the huge number of diabetic patients and the need for more accurate and automatic diagnosis, the development of deep neural networks has been acknowledged. One of the issues with deep learning classification tasks, particularly in the medical field, is a lack of labelled training data. Transfer learning enables the deep learning model to be trained with a minimal Training Dataset. In Deep convolutional networks, transfer learning can be utilized to solve the problem of insufficient training data. Previous studies on deep neural networks have been promising. Through a color fundus photography study, we have developed a model to identify the different stages of Diabetic Retinopathy using Deep learning. Our proposed model has achieved 87% of accuracy with the help of EfficientNet model. The main aim of this work is to develop a robust system for detecting DR automatically.

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“…Convolutional neural networks, an evolution in the field of neural network technologywhose job is to identify those patterns and make it as easy as possible to map each photo into its correct category It is now much easier to train deeper and larger models largely due to the widespread of model-friendly large data-sets set up for online retrieval, advanced models along with aggressive training tricks (batchnorm, RMSprop etc) and enough processing power such as GPU. S. Thora et al [3] The goal of this study's DR detection method is to apply deep learning to automatically identify the problem. Using retinal photos made available to the public by eyePACS on the Kaggle website, the model is trained on a GPU.…”

Section: Introductionmentioning

confidence: 99%

Diabetic Retinopathy Detection from Retinal Images

Zanjad

2024

IJSREM

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Diabetic Retinopathy (DR) is an eye disease that can occur in people with diabetes causing permanent or temporary blindness, where the blood vessels of retina, a layer in rear interior eye and so called light sensitive part effected by high sugar damages nerves. As I mentioned above, DR can present in various ways; Some people with sick retinas experience blurred vision, while others have difficulty seeing colors or eye floaters. At first, DR might cause no symptoms or only mild vision problems. But it can lead to blindness if left undiagnosed and untreated. This study presents a pipeline for processing and analyzing retinal images. The pipeline consists of three primary steps: (i) pre-processing images, (ii) extracting features, and (iii) classifying the results. The initial step involves pre-processing the image using various modifications to improve its quality and standardize it. Gaussian filtering has been demonstrated to work fairly well in this situation for improving the contrast of the photos. Convolutional Neural Network (CNN) based pre-trained machine learning algorithm is used to speed up the diagnosis of the severity of DR using retinal images of patients. Convolution neural networks (CNNs), among the greatest neural network architectures for image processing applications, have been utilized in the second and third stages. Keywords-Gaussian filtering,Convolutional Neural Network,Retinalimages,Diabetic retinopathy,hemorrhage,, and exudates.

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Diabetic Retinopathy Detection by means of Deep Learning

Cited by 8 publications

References 9 publications

A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images

A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images

Diabetic Retinopathy Detection From Fundus Images Using Multi-Tasking Model With EfficientNet B5

Diabetic Retinopathy Detection from Retinal Images

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