TY  - EJOU
AU  - Bilal, Anas 
AU  - Imran, Azhar 
AU  - Baig, Talha Imtiaz 
AU  - Liu, Xiaowen 
AU  - Long, Haixia 
AU  - Alzahrani, Abdulkareem 
AU  - Shafiq, Muhammad 

TI  - DeepSVDNet: A Deep Learning-Based Approach for Detecting and Classifying Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images
T2  - Computer Systems Science and Engineering

PY  - 2024
VL  - 48
IS  - 2
SN  - 

AB  - Artificial Intelligence (AI) is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy (VTDR), which is a leading cause of visual impairment and blindness worldwide. However, previous automated VTDR detection methods have mainly relied on manual feature extraction and classification, leading to errors. This paper proposes a novel VTDR detection and classification model that combines different models through majority voting. Our proposed methodology involves preprocessing, data augmentation, feature extraction, and classification stages. We use a hybrid convolutional neural network-singular value decomposition (CNN-SVD) model for feature extraction and selection and an improved SVM-RBF with a Decision Tree (DT) and K-Nearest Neighbor (KNN) for classification. We tested our model on the IDRiD dataset and achieved an accuracy of 98.06%, a sensitivity of 83.67%, and a specificity of 100% for DR detection and evaluation tests, respectively. Our proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection.
KW  - Diabetic retinopathy (DR); fundus images (FIs); support vector machine (SVM); medical image analysis; convolutional neural networks (CNN); singular value decomposition (SVD); classification

DO  - 10.32604/csse.2023.039672