[en] Diabetic retinopathy (DR) is a worldwide problem associated with the human retina. It leads to minor and major blindness and is more prevalent among adults. Automated screening saves time of medical care specialists. In this work, we have used different deep learning (DL) based 3D convolutional neural network (3D-CNN) architectures for binary and multiclass (5 classes) classification of DR. We have considered mild, moderate, no, proliferate, and severe DR categories. We have deployed two artificial data augmentation/enhancement methods: random weak Gaussian blurring and random shift along with their combination to accomplish these tasks in the spatial domain. In the binary classification case, we have found the performance of 3D-CNN architecture trained by deploying combined augmentation methods to be the best, while in the multiclass case, the performance of model trained without augmentation is the best. It is observed that the DL algorithms working with large volumes of data may achieve better performances as compared to the methods working with small volumes of data.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Tufail, Ahsan Bin
Ullah, Inam
Khan, Wali Ullah ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
Asif, Muhammad
Ahmad, Ijaz
Ma, Yong-Kui
Khan, Rahim
Ullah, Kalim
Ali, Md. Sadek
External co-authors :
yes
Language :
English
Title :
Diagnosis of Diabetic Retinopathy through Retinal Fundus Images and 3D Convolutional Neural Networks with Limited Number of Samples
Alternative titles :
[en] Diagnosis of Diabetic Retinopathy through Retinal Fundus Images and 3D Convolutional Neural Networks with Limited Number of Samples
Publication date :
November 2021
Journal title :
Wireless Communications and Mobile Computing
ISSN :
1530-8669
eISSN :
1530-8677
Publisher :
John Wiley & Sons, Hoboken, United States - New Jersey
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