scholarly journals Deep Learning for Early Diabetic Retinopathy Detection

2020 ◽  
Author(s):  
Anurag Vaidya ◽  
Joshua Stough
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Early Detection ◽  
Early Diagnosis ◽  
Blood Vessels ◽  
Diabetes Complication ◽  
Retinal Blood Vessels ◽  
Multi Class Classification ◽  
Single Neural Network

Diabetic retinopathy (DR)— a leading cause of blindness— is a diabetes complication whichcauses damage to retinal blood vessels. DR can be treated non-invasively if diagnosed early enough.However, early diagnosis requires a medical examination, which may not be possible in some demographicalregions. Much previous work has largely focused on determining the presence of DR and not the severity.Thus, the goal of this study was to develop a single neural network that could 1) detect presence or absence of DR2) perform early detection of DR 3) perform multi-class classification on DR severity.

Retinal blood vessels detection for diabetic retinopathy with Ridgelet transform and convolution neural network

2020 ◽  
Vol 18 (06) ◽  
pp. 2050048
Author(s):  
Nirmal Yadav
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Blood Vessels ◽  
Machine Learning Algorithms ◽  
Convolution Neural Network ◽  
Transform Method ◽  
Ridgelet Transform ◽  
Study Results

Applying machine learning in life sciences, especially diagnostics, has become a key area of focus for researchers. Combining machine learning with traditional algorithms provides a unique opportunity of providing better solutions for the patients. In this paper, we present study results of applying the Ridgelet Transform method on retina images to enhance the blood vessels, then using machine learning algorithms to identify cases of Diabetic Retinopathy (DR). The Ridgelet transform provides better results for line singularity of image function and, thus, helps to reduce artefacts along the edges of the image. The Ridgelet Transform method, when compared with earlier known methods of image enhancement, such as Wavelet Transform and Contourlet Transform, provided satisfactory results. The transformed image using the Ridgelet Transform method with pre-processing quantifies the amount of information in the dataset. It efficiently enhances the generation of features vectors in the convolution neural network (CNN). In this study, a sample of fundus photographs was processed, which was obtained from a publicly available dataset. In pre-processing, first, CLAHE was applied, followed by filtering and application of Ridgelet transform on the patches to improve the quality of the image. Then, this processed image was used for statistical feature detection and classified by deep learning method to detect DR images from the dataset. The successful classification ratio was 98.61%. This result concludes that the transformed image of fundus using the Ridgelet Transform enables better detection by leveraging a transform-based algorithm and the deep learning.

scholarly journals Deep Learning based Method for Multi-class Classification of Diabetic Retinopathy

2021 ◽  
Vol 2 (2) ◽  
pp. 1-5
Author(s):  
Komal Damodara ◽  
Keyword(s):  
Diabetes Mellitus ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Blood Sugar Level ◽  
Retinal Disease ◽  
Fundus Imaging ◽  
Retinal Blood Vessels ◽  
The World ◽  
Multi Class Classification

Diabetes mellitus is a form of diabetes with secondary microvascular complication leading to renal dysfunction and retinal loss also termed as diabetic retinopathy. Retinopathy is grave form of retinal disease. It is the leading cause of blindness in the world. Blockage of tiny minute retinal blood vessels due to the high blood sugar level is the reason why retinopathy leads to blindness or loss of vision. This study serves the purpose of deep learning-based diagnosis of Diabetic retinopathy using the fundus imaging of the eye. In this study architectures such as VGG 16 and VGG 19 are deployed in order to classify the images into 5 categories. The performance of the two models were compared. The highest accuracy is 77.67% when using the VGG 16 pre-trained model.

scholarly journals Diabetic Retinopathy Detection Using Neural Network

2019 ◽  
Vol 8 (10) ◽  
pp. 2936-2940
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computer Vision ◽  
Diabetic Retinopathy ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Blood Vessels ◽  
Proliferative Diabetic Retinopathy ◽  
Eye Disease ◽  
Learning Methods

Now-a-days diabetics are affecting many people and it causes an eye disease called “diabetics retinopathy” but many are not aware of that, so it causes blindness. Diabetes aimed at protracted time harms the blood vessels of retina in addition to thereby affecting seeing ability of an individual in addition to leading to diabetic retinopathy. Diabetic retinopathy is classified hooked on twofold classes, non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR). Finding of diabetic retinopathy in fundus imaginary is done by computer vision and deep learning methods using artificial neural networks. The images of the diabetic retinopathy datasets are trained in neural networks. And based on the training datasets we can detect whether the person has (i)no diabetic retinopathy, (ii) mild non-proliferative diabetic retinopathy, (iii) severe non-proliferative diabetic retinopathy and (iv) proliferative diabetic retinopathy.

scholarly journals EfficientNetB3 Architecture for Diabetic Retinopathy Assessment using  Fundus Images

2021 ◽  
Author(s):  
Abdelali ELMOUFIDI ◽  
Hind Amoun
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Fundus Images ◽  
Retinal Area ◽  
Hard Exudate ◽  
Retinal Blood Vessels

Abstract Classification of the stages of diabetic retinopathy (DR) is considered a key step in the assessment and management of diabetic retinopathy. Due to the damage caused by high blood sugar to the retinal blood vessels, different microscopic structures can be occupied in the retinal area, such as micro-aneurysms, hard exudate and neovascularization. The convolutional neural network (CNN) based on deep learning has become a promising method for the analysis of biomedical images. In this work, representative images of diabetic retinopathy (DR) are divided into five categories according to the professional knowledge of ophthalmologists. This article focuses on the use of convolutional neural networks to classify background images of DR according to disease severity and on the application of pooling, Softmax Activation to achieve greater accuracy. The aptos2019-blindness-detection database makes it possible to verify the performance of the proposed algorithm.

scholarly journals Deep Learning untuk Klasifikasi Diabetic Retinopathy menggunakan Model EfficientNet

2020 ◽  
Vol 8 (3) ◽  
pp. 693
Author(s):  
SYAMSUL RIZAL ◽  
NUR IBRAHIM ◽  
NOR KUMALASARI CAESAR PRATIWI ◽  
SOFIA SAIDAH ◽  
RADEN YUNENDAH NUR FU’ADAH
Keyword(s):  
Neural Network ◽  
Diabetes Mellitus ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Early Detection ◽  
Convolutional Neural Network ◽  
Keywords Diabetic Retinopathy ◽  
Complications Of Diabetes ◽  
Diabetes Melitus ◽  
Learning By Using

ABSTRAKDiabetic Retinopathy merupakan penyakit yang dapat mengakibatkan kebutaan mata yang disebabkan oleh adanya komplikasi penyakit diabetes melitus. Oleh karena itu mendeteksi secara dini sangat diperlukan untuk mencegah bertambah parahnya penyakit tersebut. Penelitian ini merancang sebuah sistem yang dapat mendeteksi Diabetic Retinopathy berbasis Deep Learning dengan menggunakan Convolutional Neural Network (CNN). EfficientNet model digunakan untuk melatih dataset yang telah di pre-prosesing sebelumnya. Hasil dari penelitian tersebut didapatkan akurasi sebesar 79.8% yang dapat mengklasifikasi 5 level penyakit Diabetic Retinopathy.Kata kunci: Diabetic Retinopathy, Deep Learning, CNN, EfficientNet, Diabetic Classification ABSTRACTDiabetic Retinopathy is a diseases which can cause blindness in the eyes because of the complications of diabetes mellitus. Therefore, an early detection for this diseases is very important to prevent the diseases become severe. This research builds the system which can detect the Diabetic Retinopathy based on Deep Learning by using Convolutional Neural Network (CNN). EfficientNet model is used to trained the dataset which have been pre-prossed. The result shows that the system can clasiffy the 5 level of Diabetic Retinopathy with accuracy 79.8%. Keywords: Diabetic Retinopathy, Deep Learning, CNN, EfficientNet, Diabetic Classification

scholarly journals Retinal blood vessels segmentation using classical edge detection filters and the neural network

2021 ◽  
Vol 23 ◽  
pp. 100521
Author(s):  
Beaudelaire Saha Tchinda ◽  
Daniel Tchiotsop ◽  
Michel Noubom ◽  
Valerie Louis-Dorr ◽  
Didier Wolf
Keyword(s):  
Neural Network ◽  
Edge Detection ◽  
Blood Vessels ◽  
The Neural Network ◽  
Retinal Blood Vessels
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