Choroidal Neovascularization Screening on OCT-Angiography Choriocapillaris Images by Convolutional Neural Networks

Taibouni, Kawther; Mière, Alexandra; Samake, Abdourahmane; Souied, Eric H.; Petit, Éric; Chenoune, Yasmina

doi:10.3390/app11199313

Cited by 9 publications

(6 citation statements)

References 30 publications

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“…Kawther Taibouni et al 2021 [ 172 ], uses a deep learning algorithm for the screening of CNV, this groundbreaking CNN-based tool will aid clinicians in the difficult chore of screening for neovascular problems.…”

Section: Dr Screening Methodsmentioning

confidence: 99%

Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review

Dubey

Dixit

2022

Multimed Tools Appl

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Diabetes is a long-term condition in which the pancreas quits producing insulin or the body’s insulin isn’t utilised properly. One of the signs of diabetes is Diabetic Retinopathy. Diabetic retinopathy is the most prevalent type of diabetes, if remains unaddressed, diabetic retinopathy can affect all diabetics and become very serious, raising the chances of blindness. It is a chronic systemic condition that affects up to 80% of patients for more than ten years. Many researchers believe that if diabetes individuals are diagnosed early enough, they can be rescued from the condition in 90% of cases. Diabetes damages the capillaries, which are microscopic blood vessels in the retina. On images, blood vessel damage is usually noticeable. Therefore, in this study, several traditional, as well as deep learning-based approaches, are reviewed for the classification and detection of this particular diabetic-based eye disease known as diabetic retinopathy, and also the advantage of one approach over the other is also described. Along with the approaches, the dataset and the evaluation metrics useful for DR detection and classification are also discussed. The main finding of this study is to aware researchers about the different challenges occurs while detecting diabetic retinopathy using computer vision, deep learning techniques. Therefore, a purpose of this review paper is to sum up all the major aspects while detecting DR like lesion identification, classification and segmentation, security attacks on the deep learning models, proper categorization of datasets and evaluation metrics. As deep learning models are quite expensive and more prone to security attacks thus, in future it is advisable to develop a refined, reliable and robust model which overcomes all these aspects which are commonly found while designing deep learning models.

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Section: Dr Screening Methodsmentioning

confidence: 99%

Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review

Dubey

Dixit

2022

Multimed Tools Appl

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“…Different settings were applied in full architectures, in a modified architecture, or in a transfer learning approach. VGG19, a type of CNN with 16 convolutional and 3 fully connected trainable layers with 5 max-pooling layers, was customized on some layers by Taibouni et al [59] in the application of AMD grading. In their work, a rectified linear unit (ReLU) was added as an activation function and a 2 × 2 max pooling layer after convolution layers.…”

Section: Deep Learning Architectures and Settingsmentioning

confidence: 99%

A Complete Review of Automatic Detection, Segmentation, and Quantification of Neovascularization in Optical Coherence Tomography Angiography Images

Tun,

Aimmanee

2023

Diagnostics

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Optical coherence tomography (OCT) is revolutionizing the way we assess eye complications such as diabetic retinopathy (DR) and age-related macular degeneration (AMD). With its ability to provide layer-by-layer information on the retina, OCT enables the early detection of abnormalities emerging underneath the retinal surface. The latest advancement in this field, OCT angiography (OCTA), takes this to the next level by providing detailed vascular information without requiring dye injections. One of the most significant indicators of DR and AMD is neovascularization, the abnormal growth of unhealthy vessels. In this work, the techniques and algorithms used for the automatic detection, classification, and segmentation of neovascularization in OCTA images are explored. From image processing to machine learning and deep learning, works related to automated image analysis of neovascularization are summarized from different points of view. The problems and future work of each method are also discussed.

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“…Their experimental results outperformed many existing approaches. In order to predict the severity of age-related macular degeneration, the study ( Taibouni et al, 2021 ) suggested a classification architecture based on deep learning (AMD). An ensemble of various convolutional neural networks were employed in this study to classify AMD into 13 different categories ( Ying et al, 2009 ).…”

Section: Introductionmentioning

confidence: 99%

Fundus image classification using Inception V3 and ResNet-50 for the early diagnostics of fundus diseases

et al. 2023

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Purpose: We aim to present effective and computer aided diagnostics in the field of ophthalmology and improve eye health. This study aims to create an automated deep learning based system for categorizing fundus images into three classes: normal, macular degeneration and tessellated fundus for the timely recognition and treatment of diabetic retinopathy and other diseases.Methods: A total of 1,032 fundus images were collected from 516 patients using fundus camera from Health Management Center, Shenzhen University General Hospital Shenzhen University, Shenzhen 518055, Guangdong, China. Then, Inception V3 and ResNet-50 deep learning models are used to classify fundus images into three classes, Normal, Macular degeneration and tessellated fundus for the timely recognition and treatment of fundus diseases.Results: The experimental results show that the effect of model recognition is the best when the Adam is used as optimizer method, the number of iterations is 150, and 0.00 as the learning rate. According to our proposed approach we, achieved the highest accuracy of 93.81% and 91.76% by using ResNet-50 and Inception V3 after fine-tuned and adjusted hyper parameters according to our classification problem.Conclusion: Our research provides a reference to the clinical diagnosis or screening for diabetic retinopathy and other eye diseases. Our suggested computer aided diagnostics framework will prevent incorrect diagnoses caused by the low image quality and individual experience, and other factors. In future implementations, the ophthalmologists can implement more advanced learning algorithms to improve the accuracy of diagnosis.

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Choroidal Neovascularization Screening on OCT-Angiography Choriocapillaris Images by Convolutional Neural Networks

Cited by 9 publications

References 30 publications

Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review

Recent developments on computer aided systems for diagnosis of diabetic retinopathy: a review

A Complete Review of Automatic Detection, Segmentation, and Quantification of Neovascularization in Optical Coherence Tomography Angiography Images

Fundus image classification using Inception V3 and ResNet-50 for the early diagnostics of fundus diseases

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