Neovascularization Detection on Optic Disc Region Using Deep Learning

Carrillo-Gomez, Cesar; Nakano-Miyatake, Mariko; González-H.León, Ana; Romo-Aguas, Juan C.; Quiroz‐Mercado, Hugo; Lopez-Garcia, Osvaldo

doi:10.1007/978-3-030-77004-4_11

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…They managed to demonstrate that the feature extraction method through deep learning models can yield favorable results. Carrillo-Gomez et al [26] described a method for detecting NVD using a deep learning algorithm. They evaluated several neural networks for their ability to detect NVD.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Approach for the Detection of Neovascularization in Fundus Images Using Transfer Learning

et al. 2022

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Patients with diabetes are at risk of developing a retinal disorder called Proliferative Diabetic Retinopathy (PDR). One of the main characteristics of PDR is the development of neovascularization, a condition in which abnormal blood vessels are formed on the retina. This condition can cause blindness if it is not detected and treated early. Numerous studies have proposed different image processing techniques for detecting neovascularization in fundus images. However, because of its random growth pattern and small size, neovascularization remains challenging to detect. Hence, deep learning techniques are becoming more prevalent in neovascularization identification because of their ability to perform automatic feature extraction on objects with complex features. In this paper, a method of neovascularization detection based on transfer learning is proposed. The performance of the transfer learning method is investigated using four pre-trained Convolutional Neural Network (CNN), which include AlexNet, GoogLeNet, ResNet18, and ResNet50. In addition, an improved network based on the combination of ResNet18 and GoogLeNet is proposed. Evaluation on 1174 retinal image patches showed that the proposed network could achieve 91.57%, 85.69%, 97.44%, and 97.10% of accuracy, sensitivity, specificity, and precision, respectively. We demonstrated that the proposed method outperforms each individual CNN for neovascularization detection. It also shows better performance compared to another method that utilized deep learning models for feature extraction and Support Vector Machine (SVM) for classification.

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Section: Related Workmentioning

confidence: 99%

A Deep Learning Approach for the Detection of Neovascularization in Fundus Images Using Transfer Learning

et al. 2022

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Improved PSP-Net Segmentation Network for Automatic Detection of Neovascularization in Color Fundus Images

Liu

Wang

Dai

et al. 2022

2022 IEEE International Conference on Visual Communications and Image Processing (VCIP)

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Deep Learning in Retinal Diseases Diagnosis

Aykat

Senan

2022

Machine Learning and AI Techniques in Interactive Medical Image Analysis

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Retinal diseases are among the leading causes of blindness and severe vision loss at the global level. Early diagnosis of retinal disease is of great importance in order to prevent irreversible damage to the eye. In recent years, deep learning methods have been widely used to diagnose retinal diseases. These models are developed for diagnosing a particular retinal disease. Most of these models detect and analyze disease features from the retinal image. Thanks to systems that can predict the detection of retinal diseases with high accuracy, it has allowed ophthalmologists to reduce their workload and reach more patients. This extensive literature review presents a comparative study of deep learning methods used to detect retinal diseases. For this purpose, the studies conducted on the subject between the years 2015-2022 will be examined. The related studies will be analyzed according to (1) rates of studies according to publishing years, (2) the data sets used, (3) the deep learning methods used, and (4) diagnosed retinal diseases.

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Neovascularization Detection on Optic Disc Region Using Deep Learning

Cited by 3 publications

References 13 publications

A Deep Learning Approach for the Detection of Neovascularization in Fundus Images Using Transfer Learning

A Deep Learning Approach for the Detection of Neovascularization in Fundus Images Using Transfer Learning

Improved PSP-Net Segmentation Network for Automatic Detection of Neovascularization in Color Fundus Images

Deep Learning in Retinal Diseases Diagnosis

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