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Detection of glaucoma from fundus image using pre-trained Densenet201 model

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Title Detection of glaucoma from fundus image using pre-trained Densenet201 model
 
Creator Elangovan, Poonguzhali
D, Vijayalakshmi
Nath, Malaya Kumar
 
Subject Deep learning
Fine-tuning
Ocular disease
Transfer learning
 
Description 33-39
In recent years, the performance of deep learning algorithms for image recognition has improved tremendously. The
inherent ability of a convolutional neural network has made the task of classifying glaucoma and normal fundus images
more appropriately. Transferring the weights from the pre-trained model resulted in faster and easier training than training
the network from scratch. In this paper, a dense convolutional neural network (Densenet201) has been utilized to extract the
relevant features for classification. Training with 80% of the images and testing with 20% of the images has been
performed. The performance metrics obtained by various classifiers such as softmax, support vector machine (SVM), knearest
neighbor (KNN), and Naive Bayes (NB) have been compared. Experimental results have shown that the softmax
classifier outperformed the other classifiers with 96.48% accuracy, 98.88% sensitivity, 92.1% specificity, 95.82% precision,
and 97.28% F1-score, with DRISHTI-GS1 database. An increase in the classification accuracy of about 1% has been
achieved with enhanced fundus images.
 
Date 2021-09-09T10:12:55Z
2021-09-09T10:12:55Z
2021-03
 
Type Article
 
Identifier 0975-105X (Online); 0367-8393 (Print)
http://nopr.niscair.res.in/handle/123456789/58080
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source IJRSP Vol.50(1) [March 2021]