scholarly journals Automatic Detection and stage classification of Diabetic Retinopathy Using Convolutional neural network with densenet120

2021 ◽  
Author(s):  
E. Balaji
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Automatic Detection ◽  
Stage Classification

Convolutional Neural Network for Classification of Diabetic Retinopathy Grade

2020 ◽  
pp. 104-118
Author(s):  
Vanessa Alcalá-Rmz ◽  
Valeria Maeda-Gutiérrez ◽  
Laura A. Zanella-Calzada ◽  
Adan Valladares-Salgado ◽  
José M. Celaya-Padilla ◽  
...  
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network

scholarly journals Automatic Detection and Classification of Rib Fractures on Thoracic CT Using Convolutional Neural Network: Accuracy and Feasibility

2020 ◽  
Vol 21 (7) ◽  
pp. 869 ◽  
Author(s):  
Qing-Qing Zhou ◽  
Jiashuo Wang ◽  
Wen Tang ◽  
Zhang-Chun Hu ◽  
Zi-Yi Xia ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Automatic Detection ◽  
Rib Fractures ◽  
Thoracic Ct

scholarly journals Comparison of Convolutional Neural Network Model in Classification of Diabetic Retinopathy

2019 ◽  
Vol 9 (2) ◽  
pp. 141
Author(s):  
Hartanto Ignatius ◽  
Ricky Chandra ◽  
Nicholas Bohdan ◽  
Abdi Dharma
Keyword(s):  
Neural Network ◽  
Diabetes Mellitus ◽  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Contrast Enhancement ◽  
Neural Network Model ◽  
Reasonable Accuracy ◽  
Deep Architecture

<p class="JGI-AbstractIsi">Untreated diabetes mellitus will cause complications, and one of the diseases caused by it is Diabetic Retinopathy (DR). Machine learning is one of the methods that can be used to classify DR. Convolutional Neural Network (CNN) is a branch of machine learning that can classify images with reasonable accuracy. The Messidor dataset, which has 1,200 images, is often used as a dataset for the DR classification. Before training the model, we carried out several data preprocessing, such as labeling, resizing, cropping, separation of the green channel of images, contrast enhancement, and changing image extensions. In this paper, we proposed three methods of DR classification: Simple CNN, Le-Net, and DRnet model. The accuracy of testing of the several models of test data was 46.7%, 51.1%, and 58.3% Based on the research, we can see that DR classification must use a deep architecture so that the feature of the DR can be recognized. In this DR classification, DRnet achieved better accuracy with an average of 9.4% compared to Simple CNN and Le-Net model.</p>

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.

Sign in / Sign up

Export Citation Format

Share Document