Automatic detection of non-proliferative diabetic retinopathy in retinal fundus images using convolution neural network

2020 ◽  
Author(s):  
P. Saranya ◽  
S. Prabakaran
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Proliferative Diabetic Retinopathy ◽  
Automatic Detection ◽  
Convolution Neural Network ◽  
Fundus Images ◽  
Retinal Fundus Images ◽  
Retinal Fundus

scholarly journals Transfer Learning Based MA Detection (TL-MAD)

2020 ◽  
Vol 9 (3) ◽  
pp. 3653-3656
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Automatic Detection ◽  
Microvascular Complication ◽  
Fundus Images ◽  
Main Symptom ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Diabetic Retinopathy (DR) is a microvascular complication of Diabetes that can lead to blindness if it is severe. Microaneurysm (MA) is the initial and main symptom of DR. In this paper, an automatic detection of DR from retinal fundus images of publicly available dataset has been proposed using transfer learning with pre-trained model VGG16 based on Convolutional Neural Network (CNN). Our method achieves improvement in accuracy for MA detection using retinal fundus images in prediction of Diabetic Retinopathy.

scholarly journals Automatic detection and classification of diabetic retinopathy from retinal fundus images

2021 ◽  
Author(s):  
Abdullah Biran
Keyword(s):  
Diabetic Retinopathy ◽  
Gabor Filter ◽  
Automatic Detection ◽  
Histogram Equalization ◽  
Support Vector ◽  
Svm Classifier ◽  
Fundus Images ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Automatic Detection and Classification of Diabetic Retinopathy from Retinal Fundus Images by Abdullah Biran, Master of Applied Science, lectrical and computer engineering Department, Ryerson University, 2017. Diabetic Retinopathy (DR) is an eye disease that leads to blindness when it progresses to proliferative level. The earliest signs of DR are the appearance of red and yellow lesions on the retina called hemorrhages and exudates. Early diagnosis of DR prevents from blindness. In this thesis, an automatic algorithm for detecting diabetic retinopathy is presented. The algorithm is based on combination of several image processing techniques including Circular Hough Transform (CHT), Contrast Limited Adaptive Histogram Equalization (CLAHE), Gabor filter and thresholding. In addition, Support Vector Machine (SVM) classifier is used to classify retinal images into normal or abnormal cases of DR including non-proliferative (NPDR) or proliferative diabetic retinopathy (PDR). The proposed method has been tested on fundus images from Standard Diabetic Retinopathy Database (DIARETDB). The implementation of the presented methodology was done in MATLAB. The methodology is tested for sensitivity and accuracy.

scholarly journals Detection of Proliferative Diabetic Retinopathy in Fundus Images Using Convolution Neural Network

2020 ◽  
Vol 769 ◽  
pp. 012029
Author(s):  
Hasliza Abu Hassan ◽  
Marzuqi Yaakob ◽  
Sasni Ismail ◽  
Juwairiyyah Abd Rahman ◽  
Izyani Mat Rusni ◽  
...  
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Proliferative Diabetic Retinopathy ◽  
Convolution Neural Network ◽  
Fundus Images

scholarly journals Pengaruh Preprocessing Terhadap Klasifikasi Diabetic Retinopathy dengan Pendekatan Transfer Learning Convolutional Neural Network

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Juan Elisha Widyaya ◽  
Setia Budi
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Early Stage ◽  
Careful Examination ◽  
Fundus Images ◽  
Fundus Image ◽  
Retinal Fundus Image ◽  
Retinal Fundus Images ◽  
Retinal Fundus

Diabetic retinopathy (DR) is eye diseases caused by diabetic mellitus or sugar diseases. If DR is detected in early stage, the blindness that follow can be prevented. Ophthalmologist or eye clinician usually decide the stage of DR from retinal fundus images. Careful examination of retinal fundus images is time consuming task and require experienced clinicians or ophthalmologist but a computer which has been trained to recognize the DR stages can diagnose and give result in real-time manner. One approach of algorithm to train a computer to recognize an image is deep learning Convolutional Neural Network (CNN). CNN allows a computer to learn the features of an image, in our case is retinal fundus image, automatically. Preprocessing is usually done before a CNN model is trained. In this study, four preprocessing were carried out. Of the four preprocessing tested, preprocessing with CLAHE and unsharp masking on the green channel of the retinal fundus image give the best results with an accuracy of 79.79%, 82.97% precision, 74.64% recall, and 95.81% AUC. The CNN architecture used is Inception v3.

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