Imaging in inherited retinal disorders

Müftüoğlu, İlkay Kılıç; Al-Sheikh, Mayss; Sushma, J; Rasheed, Mohammed Abdul; Singh, Sumit Randhir; Chhablani, Jay

doi:10.1177/1120672121990578

Cited by 5 publications

(3 citation statements)

References 139 publications

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“…This issue is solved by applying a strategy of batch balancing. Here, the batch is separated in to five varied groups like [1,2], [2,3], [3,4], [4,5] and [5,6]. A similar number of occurrences are randomly chosen during the construction of a mini-batch, i.e., 25 from all the groups and generate the batch fully balanced.…”

Section: Batch Equivalencementioning

confidence: 99%

“…A global survey by the WHO (World Health Organization) estimated that among 285 million people, 39 million are blind, and the remaining are reported to other VIs [3]. Some of the retinal disorders are Cataract, DR (Diabetic Retinopathy), Glaucoma, AMD (Aged Macular Degeneration), RP (Retinitis Pigmentosa), [4][5] etc. Cataracts are the leading cause of vision loss in aged and younger individuals [6][7].…”

Section: Introductionmentioning

confidence: 99%

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Eye-Vision Net: Cataract Detection and Classification in Retinal and Slit Lamp Images using Deep Network

Saju¹,

R²

2022

IJACSA

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In the modern world, cataracts are the predominant cause of blindness. Early treatment and detection can reduce the number of cataract patients and prevent surgery. However, cataract grade classification is necessary to control risk and avoid blindness. Previously, various studies focused on developing a system to detect cataract type and grade. However, the existing works on cataract detection does not provide optimal results because of high detection error, lack of learning ability, computational complexity issues, etc. Therefore, the proposed work aims to develop an effective deep learning techniques for detecting and classifying cataracts from the given input samples. Here, the cataract detection and classification are performed using two phases. In order to provide an accurate cataract detection, the proposed study introduced Deep Optimized Convolutional Recurrent Network_Improved Aquila Optimization (Deep OCRN_IAO) model in phase I. Here, both retinal and slit lamp images are utilized for cataract detection. Then, the performance of these two image datasets are analysed, and the best one is chosen for cataract type and grade classification. By analysing the performance, the slit lamp images attain higher results. Therefore, phase II uses slit lamp images and detects the type and grade of cataracts through the proposed Batch Equivalence ResNet-101 (BE_ResNet101) model. The proposed classification model is highly efficient to classify the type and grades of cataracts. The experimental setup is done using MATLAB software, and the datasets used for simulation purposes are DRIMDB (Diabetic Retinopathy Images Database) and real-time slit lamp images. The proposed type and grade detection model has an accuracy of 98.87%, specificity of 99.66%, the sensitivity of 98.28%, Youden index of 95.04%, Kappa of 97.83%, and F1-score is 95.68%. The obtained results and comparative analysis proves that the proposed model is highly suitable for cataract detection and classification.

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Section: Batch Equivalencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Eye-Vision Net: Cataract Detection and Classification in Retinal and Slit Lamp Images using Deep Network

Saju¹,

R²

2022

IJACSA

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show abstract

“…Dear Editor, I read with great interest a recent article published in the European Journal of Ophthalmology by Muftuoglu et al 1 that focused on novel multimodal imaging characteristics of patients with inherited retinal disorders. In the beginning, I would like to congratulate the authors on their article, however, I have several comments regarding their article on the topic of gyrate atrophy (GA).…”

mentioning

confidence: 99%