Automated grading of diabetic retinopathy using CNN with hierarchical clustering of image patches by siamese network

Deepa, V.; Kumar, C. Sathish; Cherian, Thomas

doi:10.1007/s13246-022-01129-z

Cited by 12 publications

(5 citation statements)

References 33 publications

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“…The first ophthalmic AI device, IDx-DR, was approved for listing with landmark significance on 11 April 2018, opening a new chapter on the combination of AI with ophthalmology. Since then, the application of AI in the ophthalmology has entered a new stage of development, leading to a series of satisfactory research results in the diagnosis, classification, recognition, and screening of ophthalmic diseases, such as diabetic retinopathy (Deepa et al, 2022;Hardas et al, 2022;Zhang et al, 2022) , retinopathy of prematurity (Coyner et al, 2022;, glaucoma Xiong et al, 2022), and retinal vein occlusion Zhang et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence Applications in Chronic Ocular Diseases

2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence Applications in Chronic Ocular Diseases

2024

Frontiers Research Topics

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“…Dong et al [23] have implemented an InceptionV3-VGG-16 based hybrid CNN through feature concatenation and have achieved 96.11% accuracy on 2693 images only whereas the proposed model has individually achieved an accuracy of 99.03% and 73.72% in InceptionV3 and 90.91% and 62.07% in VGG-16 using 35,126 images, which is significantly and comparatively larger to fit in a data hungry CNN. Deepa et al [18] and Deepa et al [19] have implemented a fine-tuned InceptionV3 and Xception based multi-stage patch-based and image-based CNN and have achieved an accuracy of 96.2% and 96%, respectively using a computationally powerful [16] have implemented Squeeze-and-Excitation CNN using limited DIARETDB1 and local dataset and achieved an accuracy of 96.92% whereas DRFEC has consistently achieved an accuracy of more than 98% and 70% on 35,126 images. Sau and Bansal [70] have implemented a FNU-GOA-MDNN for optimization using a comparatively limited ISBI 2018 IDRiD dataset and have achieved an accuracy of 95.27%, whereas DRFEC has consistently achieved an accuracy of more than 98% and 70% on 35,126 images.…”

Section: Discussionmentioning

confidence: 99%

“…It uses 1, 40,000 fundus images from publicly available EyePACs dataset and 1200 fundus images from Shanghai General Hospital. Deepa et al [19] Sau and Bansal [70] have proposed a Fitness based Newly Updated Grasshopper Optimization Algorithm (FNU-GOA) for the optimization of a DL model and to optimize the threshold value in active contour method for the segmentation of blood vessels, MAs, EXs and HEs for DR detection. It is compared with several other optimization algorithms such as Particle Swarm Optimization (PSO), Grey wolf optimization algorithm (GWO), Whale optimization algorithm (WOA) and Grasshopper Optimization Algorithm (GOA), and ML classifiers such as Neural Network (NN), RNN, Long Short Term Memory (LSTM) and Deep NN.…”

Section: The Proposed Model Identifies High Bias and High Variance Du...mentioning

confidence: 99%

“…On this basis we reject our null hypothesis which claims on choosing and finding a best DL model for image classification, and accept our alternative hypothesis of finding and engineering a better DL model for DR image classification. Figures 61 and 62 depicts the plot of KSPA statistical test for 26 DL models, and comparison of them with contemporary and state-of-the-art works [1, 16,18,19,23,70,72,82], respectively.…”

Section: Inceptionv3mentioning

confidence: 99%

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Detection of Diabetic Retinopathy using Convolutional Neural Networks for Feature Extraction and Classification (DRFEC)

Das

Biswas

Bandyopadhyay

2022

Multimed Tools Appl

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Diabetic Retinopathy (DR) is caused as a result of Diabetes Mellitus which causes development of various retinal abrasions in the human retina. These lesions cause hindrance in vision and in severe cases, DR can lead to blindness. DR is observed amongst 80% of patients who have been diagnosed from prolonged diabetes for a period of 10–15 years. The manual process of periodic DR diagnosis and detection for necessary treatment, is time consuming and unreliable due to unavailability of resources and expert opinion. Therefore, computerized diagnostic systems which use Deep Learning (DL) Convolutional Neural Network (CNN) architectures, are proposed to learn DR patterns from fundus images and identify the severity of the disease. This paper proposes a comprehensive model using 26 state-of-the-art DL networks to assess and evaluate their performance, and which contribute for deep feature extraction and image classification of DR fundus images. In the proposed model, ResNet50 has shown highest overfitting in comparison to Inception V3, which has shown lowest overfitting when trained using the Kaggle’s EyePACS fundus image dataset. EfficientNetB4 is the most optimal, efficient and reliable DL algorithm in detection of DR, followed by InceptionResNetV2, NasNetLarge and DenseNet169. EfficientNetB4 has achieved a training accuracy of 99.37% and the highest validation accuracy of 79.11%. DenseNet201 has achieved the highest training accuracy of 99.58% and a validation accuracy of 76.80% which is less than the top-4 best performing models.

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“…The first ophthalmic AI device, IDx-DR, was approved for listing with landmark significance on 11 April 2018, opening a new chapter on the combination of AI with ophthalmology. Since then, the application of AI in the ophthalmology has entered a new stage of development, leading to a series of satisfactory research results in the diagnosis, classification, recognition, and screening of ophthalmic diseases, such as diabetic retinopathy (Deepa et al, 2022;Hardas et al, 2022;, age-related macular degeneration (Glaret Subin and Muthukannan, 2022;Sotoudeh-Paima et al, 2022;, retinopathy of prematurity (Coyner et al, 2022;Wu et al, 2022), glaucoma (Dong et al, 2022;Xiong et al, 2022), and retinal vein occlusion (Miao et al, 2022;Ren et al, 2022;.…”

Section: Introductionmentioning

confidence: 99%

Advances in artificial intelligence models and algorithms in the field of optometry

Wang

Wen

et al. 2023

Front. Cell Dev. Biol.

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The rapid development of computer science over the past few decades has led to unprecedented progress in the field of artificial intelligence (AI). Its wide application in ophthalmology, especially image processing and data analysis, is particularly extensive and its performance excellent. In recent years, AI has been increasingly applied in optometry with remarkable results. This review is a summary of the application progress of different AI models and algorithms used in optometry (for problems such as myopia, strabismus, amblyopia, keratoconus, and intraocular lens) and includes a discussion of the limitations and challenges associated with its application in this field.

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Automated grading of diabetic retinopathy using CNN with hierarchical clustering of image patches by siamese network

Cited by 12 publications

References 33 publications

Artificial Intelligence Applications in Chronic Ocular Diseases

Artificial Intelligence Applications in Chronic Ocular Diseases

Detection of Diabetic Retinopathy using Convolutional Neural Networks for Feature Extraction and Classification (DRFEC)

Advances in artificial intelligence models and algorithms in the field of optometry

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