Detection of diabetic retinopathy using deep learning methodology

Mushtaq, Gazala; Siddiqui, Farheen

doi:10.1088/1757-899x/1070/1/012049

Cited by 64 publications

(22 citation statements)

References 14 publications

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“…Their proposed methodology creates the basis for the exploration of higher-level computer-aided diagnostic algorithms for automated DR detection and classification. Similarly, Mushtaq et al, [15] apply a Densely Connected Convolutional Network for the early detection of DR. The proposed methodology provides a robust system for automated DR detection accomplished through a series of steps including data collection, preprocessing, augmentation, and modeling.…”

Section: Related Workmentioning

confidence: 99%

Continuous Eye Disease Severity Evaluation System using Siamese Neural Networks

AbdulRaheem¹,

Oladipo²,

Ajagbe³

et al. 2023

paradigmplus

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Evaluating the severity of eye diseases using medical images is a very essential and routine task performed in medical diagnosis and treatment. Current grading systems which are largely based on discrete classification are unreliable and do reflect not the entire spectrum of eye disease severity. The unreliability of discrete classification systems for eye diseases is clear, as classification is subjective and done based on the personal opinion of various medical experts, which may vary. In a bid to solve these issues, this study proposes a system for determining the severity of eye diseases on a continuous range using a twin-convoluted neural network approach known as Siamese Neural Networks. This system is demonstrated in the domain of diabetic retinopathy. Samples of retinal fundus images from an eye clinic in India are taken as test cases to evaluate the performance of a Siamese Triplet network which attempts to find the distance between their image embedding. The outputs of the Siamese network when a reference image is juxtaposed with a collection of images with distant severity categories (negative images), as well as when two reference images are compared to each other, are found to have a positive correlation (95\%) with originally assigned severity classes. Hence, these outputs indicate a continuous range of the severity and change in eye diseases.

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Section: Related Workmentioning

confidence: 99%

Continuous Eye Disease Severity Evaluation System using Siamese Neural Networks

AbdulRaheem¹,

Oladipo²,

Ajagbe³

et al. 2023

paradigmplus

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“…The model's accuracy came in at 95.8%. Authors of [24] used DenseNet169 for the classification of Diabetic Retinopathy using two publicly available datasets [9], [10]. Their proposed model achieved an accuracy of 90%.…”

Section: B Diabetic Retinopathy Using Deep Learningmentioning

confidence: 99%

Diabetic Retinopathy Classification using VisionTransformer Architectures and Deep Learning

Hossain,

Chakrabarty,

Alam

2023

Preprint

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Diabetic Retinopathy (DR) is a vision-threatening disease that has affected plenty of people worldwide and is treated using digital fundus imaging and retinal images by specialists at the primary level. DR is one of the most common eye diseases and it is caused by long-term diabetes and high blood pressure.A lot of research has been carried out in the recent past using Deep Learning (DL) models on eye disease diagnosis. However, recently Transformer based models have proved to be quite dynamic in terms of image classification and have outperformed DL architectures in many cases. In this research, we implement a vision Transformer (ViT) model with Shifted Patch Tokenization(SPT) and Locality Self Attention (LSA) that is specifically built to perform well on small datasets, alongside we implement someConvolutional Neural Network (CNN) based Transfer learning algorithms such as ResNet101, InceptionV3, and Xception. Upon training and testing the selected architectures, we find that theViT-based model outperforms all the state-of-the-art DL models ,attaining accuracies of 82.26% and 77.01% for training and validation respectively. Furthermore, we used StyleGAN2 as an alternative to the generic data augmentation technique to produce images that replicate the original properties of the real images.We augmented our dataset consisting of 2750 images usingStyleGAN2 and extended it into a total of 5000 images keeping an equal number of images in each of the classes mitigating class imbalance and ensuring the dataset is small enough to demonstrate ViT on small datasets.

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“…The literature covers a large number of DL-driven applications for clinical diagnosis in ophthalmology. Recently, several studies have been conducted on deep learning for the early detection of diseases and eye disorders, which include diabetic retinopathy detection [ 17 , 18 ], glaucoma diagnosis [ 19 , 20 ], and the automated identification of myopia using eye fundus images [ 21 ]. All these DL-based applications have high clinical relevance and may prove effective in supporting the design of suitable protocols in ophthalmology.…”

Section: Related Workmentioning

confidence: 99%

A Fully Unsupervised Deep Learning Framework for Non-Rigid Fundus Image Registration

Benvenuto

Colnago²,

Dias

et al. 2022

Bioengineering

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In ophthalmology, the registration problem consists of finding a geometric transformation that aligns a pair of images, supporting eye-care specialists who need to record and compare images of the same patient. Considering the registration methods for handling eye fundus images, the literature offers only a limited number of proposals based on deep learning (DL), whose implementations use the supervised learning paradigm to train a model. Additionally, ensuring high-quality registrations while still being flexible enough to tackle a broad range of fundus images is another drawback faced by most existing methods in the literature. Therefore, in this paper, we address the above-mentioned issues by introducing a new DL-based framework for eye fundus registration. Our methodology combines a U-shaped fully convolutional neural network with a spatial transformation learning scheme, where a reference-free similarity metric allows the registration without assuming any pre-annotated or artificially created data. Once trained, the model is able to accurately align pairs of images captured under several conditions, which include the presence of anatomical differences and low-quality photographs. Compared to other registration methods, our approach achieves better registration outcomes by just passing as input the desired pair of fundus images.

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Detection of diabetic retinopathy using deep learning methodology

Cited by 64 publications

References 14 publications

Continuous Eye Disease Severity Evaluation System using Siamese Neural Networks

Continuous Eye Disease Severity Evaluation System using Siamese Neural Networks

Diabetic Retinopathy Classification using VisionTransformer Architectures and Deep Learning

A Fully Unsupervised Deep Learning Framework for Non-Rigid Fundus Image Registration

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