International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 06 | June 2022
p-ISSN: 2395-0072
www.irjet.net
Detection of Diabetic Retinopathy using Convolutional Neural Network J.Divya1, Roma B Das2, Shreya R Mummigatti3, Shweta Gudur4, Dr. Jagadeesh Pujari5 1234Student,
Dept of Information Science Engineering, SDMCET, Dharwad, Karnataka, India. Dept of Information Science Engineering, SDMCET, Dharwad, Karnataka, India. ---------------------------------------------------------------------***--------------------------------------------------------------------5Professor,
Abstract – Diabetic Retinopathy is a micro vascular
diseased based on the application of haar wavelet and first order histogram features.
disorder that occurs due to the effect of diabetes that causes vision damage to the retina, eventually leads to blindness. Early detection of diabetic retinopathy protects the patients from losing their vision. Detecting diabetic retinopathy manually consumes lot of time and patients have to undergo series of medical examinations. Hence, an automated system helps in an easy and quick detection of diabetic retinopathy. The proposed system inputs the image, extracts the features such as micro aneurysms, exudates and haemorrhages and classifies input image using convolution neural network as normal or diseased based on its severity level. The result obtained from the proposed method gives an accuracy value of 83%. And the system also predicts the severity level as mild, moderate, proliferate and severe.
Image processing technique are proposed here which introduces a computer assisted diagnosis based on the digital processing of retinal images.[2] in order to help people detecting diabetic retinopathy in advance. The main goal is to automatically classify the grade of non-proliferative diabetic retinopathy at any retinal image. For that, an initial image processing stage isolates blood vessels, micro aneurysms and hard exudates in order to extract features that can be used by a support vector machine to figure out the retinopathy grade of each retinal image. Several image pre-processing techniques have also been proposed in order to detect diabetic retinopathy. However, despite all these previous works, automated detection of diabetic retinopathy still remains a field for improvement. Thus, this paper proposes a new computer assisted diagnosis based on the digital processing of retinal images in order to help people detecting diabetic retinopathy in advance.
Key Words: Diabetic Retinopathy (DR), Convolutional Neural Network(CNN).
1. INTRODUCTION
An automated computer aided system is proposed in [3] for the detection of DR using machine learning hybrid models by extracting the features like micro aneurysms, hemorrhages and hard exudates. The classifier used in this proposed model is the hybrid combination of SVM and KNN. Early medical diagnosis of DR and its medical cure is essential to prevent the severe side effects of DR. Manual detection of DR by ophthalmologist takes lot of time and the patients need to suffer lot. Hence, a system like this which is automated can help in detection of DR quickly and we can easily follow up treatment to avoid further effect to the eye.
There are approximately ninety three million people suffering with diabetic retinopathy worldwide. This number seems to increase exponentially day by day. DR involves different degrees of micro aneurysms, hemorrhages and hard exudates in the peripheral retina. There exists various effective treatments that would reduce the development of the disease provided it is diagnosed in the initial stages itself. The development of DR is a gradual process. There are various symptoms of DR such as fluctuating and blurred vision, dark spots and sudden vision loss. A web application is implemented in this paper which automatically detects DR when an image is uploaded and is capable of classifying the images based on the severity levels.
A robust automated system [3] is proposed which detects and classifies the different stages of DR. The raw fundus images are processed for removing noise and converting these images into gray images using the steps for preprocessing to ensure easier post processing. The optic disc and retinal nerves are segmented. The features extracted are taken as input parameters for the classification model for classifying the images. The retinal nerves and optic disc are segmented, and using Gray Level Co-occurrence Matrix (GLCM) method the features are being extracted. An UI is implemented that contains a push button to load an image which is used to upload the input image then by using the radio button present in the classifier is to be chosen by the user for the classification process. After the classifier is chosen the processing button is pressed which will process and show all the processed images with the result of the classification as the stage of the Diabetic Retinopathy predicted.
1.1 Literature Survey Abnormality Detection in retinal images using Haar wavelet and First order features [1]. A machine learning technique has been a reliable one. The method classifies abnormality detection in retinal images using Haar wavelet and First order features. Diaretdb0 and Diaretdb1 are used as the databases. A comparative study of Decision tree classifier and a KNN classifier is performed. Encouraging results were found with the classification accuracy of 85% with K-Nearest Neighbor classifier and 75% accuracy with decision tree classifier. An automated detection of DR is done on real time basis. The system proposed in this paper is computationally inexpensive. The retinal image is classified as healthy or © 2022, IRJET
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