scholarly journals Validation of Deep Convolutional Neural Network-based algorithm for detection of diabetic retinopathy – Artificial intelligence versus clinician for screening

2020 ◽  
Vol 68 (2) ◽  
pp. 398 ◽  
Author(s):  
Payal Shah ◽  
DivyanshK Mishra ◽  
MaheshP Shanmugam ◽  
Bindiya Doshi ◽  
Hariprasad Jayaraj ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network

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>

The impact of deep convolutional neural network‐based artificial intelligence on colonoscopy outcomes: A systematic review with meta‐analysis

2020 ◽  
Vol 35 (10) ◽  
pp. 1676-1683 ◽  
Author(s):  
Muhammad Aziz ◽  
Rawish Fatima ◽  
Charles Dong ◽  
Wade Lee‐Smith ◽  
Ali Nawras
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Systematic Review ◽  
Convolutional Neural Network ◽  
Meta Analysis ◽  
Deep Convolutional Neural Network ◽  
The Impact

scholarly journals Artificial intelligence in tongue diagnosis: Using deep convolutional neural network for recognizing unhealthy tongue with tooth-mark

2020 ◽  
Vol 18 ◽  
pp. 973-980
Author(s):  
Xu Wang ◽  
Jingwei Liu ◽  
Chaoyong Wu ◽  
Junhong Liu ◽  
Qianqian Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Tongue Diagnosis

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
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