International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 06 | Jun 2023
p-ISSN: 2395-0072
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A Deep Learning Approach for the Detection and Identification of Neovascularization in Fundus Images Sheik Arshad1, Rahul Paul2, Birali Prasanthi3, K Mahesh Kumar4 1 Student, Bachelors in CSE, Mahatma Gandhi Institute of Technology, Hyderabad, Telangana, India 2 Student, Bachelors in CSE, Mahatma Gandhi Institute of Technology, Hyderabad, Telangana, India
3Assistant Professor, Dept. of Computer Science and Engineering, Mahatma Gandhi Institute of Technology,
Hyderabad, Telangana, India
4Associate Professor, Dept. of Computer Science and Engineering, Mahatma Gandhi Institute of Technology,
Hyderabad, Telangana, India ----------------------------------------------------------------------***--------------------------------------------------------------------images can be challenging due to the presence of various confounding factors such as image noise, variability in image quality, and the presence of other retinal abnormalities. Hence, developing automated and accurate methods for detecting neovascularization in fundus images is essential.
Abstract – Diabetic patients are at a high risk of developing
a retinal disorder called Proliferative Diabetic Retinopathy (PDR). In PDR Neovascularization is considered as one of the major conditions in which there is an abnormal random growth of blood vessels on the retina. Neovascularization can cause severe vision loss and blindness if it is not detected and treated in its early stages. Fundus images include the images of the rear of an eye. Using these fundus images Neovascularization can be detected and classified into several stages. Neovascularization has a small size and random abnormal growth pattern. This could be a challenging task to detect with normal Image processing techniques. Deep learning methods can be used to detect Neovascularization because of their ability to perform automatic feature extraction on objects with complex features. The proposed system is implemented based on the performance of popular pre-trained deep neural networks such as Inception ResNetV2, DenseNet, ResNet50, ResNet18, AlexNet, and VGG19 networks. The best Convolutional Neural Network (CNN) model can be used to build and implement the Neovascularization detection and classification Model.
Deep learning techniques, particularly convolutional neural networks (CNNs), have shown remarkable success in various image-processing tasks, including medical image analysis. These techniques can learn complex features from the input images and detect the presence of Neovascularization and also classify them into different categories(Mild, Moderate, Healthy, Proliferate, Severe).
Key Words: CNN, Deep Learning, Neovascularization, Fundus Images, Pre-trained Networks.
The proposed approach for neovascularization detection in fundus images combines deep learning and image processing techniques to develop a reliable and accurate system. By using a minimum amount of fundus image dataset, the deep learning model can learn to identify and classify the condition of neovascularization in fundus images with a finer accuracy. Overall, the proposed approach can help improve the early detection and management of neovascularization, leading to better patient outcomes and reduced healthcare costs.
1. INTRODUCTION
1.1 LITERATURE SURVEY
Neovascularization is the formation of abnormal blood vessels in the retina, which can be a complication of various retinal diseases, including diabetic retinopathy. The presence of neovascularization can lead to severe vision loss and blindness if left untreated. Thus, early detection and timely intervention are crucial for preventing or minimizing the damage caused by this condition. The Neovascularization is further classified into 5 conditions - Healthy Eye, Mild, Moderate, Proliferate, and Severe.
Several research studies have proposed divergent image processing methods to detect Neovascularization in fundus images. But it is still a challenging task to detect Neovascularization due to its abnormal random growth and small size pattern. Recently, Deep learning techniques are getting immensely popular due to their advancement in Artificial Intelligence in Biomedical Image Processing. These methods can be employed to train a CNN model to detect and classify Neovascularization in fundus Images.
Fundus images correspond to the retinal view of an eye which represents the rear of an eye. These images are mostly used for detecting eye-related disorders. Fundus images are commonly used for the screening and diagnosis of diabetic retinopathy. However, detecting neovascularization in these
1) By the literature survey of the research papers, the existing system methods which include image processing and machine learning algorithms provide preferable accuracy for detecting Neovascularization, but this is limited. The abnormal random growth pattern of Neovascularization
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