Automatic diagnosis of diabetic retinopathy with the aid of adaptive average filtering with optimized deep convolutional neural network

2020 ◽  
Vol 30 (4) ◽  
pp. 1173-1193 ◽  
Author(s):  
TV Roshini ◽  
Ranjith V Ravi ◽  
A Reema Mathew ◽  
Anoop Balakrishnan Kadan ◽  
Perumal Sankar Subbian
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Automatic Diagnosis

scholarly journals Facile Diabetic Retinopathy Detection using MRHE-FEED and Classification using Deep Convolutional Neural Network

2021 ◽  
Author(s):  
Muhammad Zubair
Keyword(s):  
Neural Network ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Single Step ◽  
Deep Convolutional Neural Network ◽  
Fundus Photography ◽  
Classification Methods ◽  
Testing Data ◽  
Structured Analysis ◽  
Processing Techniques

<div><div><div><p>Diabetic Retinopathy (DR) is an intricacy of dia- betes that affects the eyes. In this paper, we have proposed a hybrid pre-processing and feature extraction technique named as Microaneurysm Retinal vein Haemorrhage Exudate (MRHE) extraction using Feature Enhancement and Edge Detection (FEED) which can extract all the features in a single step and with very less complexity. To classify the presence of DR, we have used an efficient Deep Convolutional Neural Network (D-CNN), model. The D-CNN model is trained with four salient features namely retinal veins, MA’s, exudates, and haemorrhages which were extracted from the raw images using image-processing techniques. After training and testing the D-CNN model, we were able to classify the presence of DR based on the features extracted from the testing data. To implement this proposed method, we have used a dataset from the STructured Analysis of the Retina (STARE) Database, which comprises of retinal images taken under various imaging conditions using fundus photography. To demonstrate the legitimacy of the proposed method, we have compared our method with the existing DR detection and classification methods such as SVM, ANN,etc.. Performance evaluation results in terms of Accuracy and Recall show that the proposed algorithm outperforms other existing DR classification methods.</p></div></div></div>

Automatic diagnosis of fungal keratitis using data augmentation and image fusion with deep convolutional neural network

2020 ◽  
Vol 187 ◽  
pp. 105019 ◽  
Author(s):  
Zhi Liu ◽  
Yankun Cao ◽  
Yujun Li ◽  
Xiaoyan Xiao ◽  
Qingchen Qiu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Fusion ◽  
Data Augmentation ◽  
Fungal Keratitis ◽  
Deep Convolutional Neural Network ◽  
Automatic Diagnosis ◽  
Using Data

scholarly journals Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network

2017 ◽  
Vol 16 (1) ◽  
Author(s):  
Jiewei Jiang ◽  
Xiyang Liu ◽  
Kai Zhang ◽  
Erping Long ◽  
Liming Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Automatic Diagnosis
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