Diabetic Retinopathy (DR) is considered as the complication of Diabetes Mellitus that damages the blood vessels in the retina. This is characterized as a serious vision-threatening problem in most of the diabetic subjects. Effective automatic classification of diabetic retinopathy is a challenging task in the medical field. The feature extraction plays an eminent role in the effective classification of disease. The proposed work focuses on the extraction of Haralick and Anisotropic Dual-Tree Complex Wavelet Transform (ADTCWT) features that can perform reliable DR classification from retinal fundus images. The Haralick features are based on second-order statistics and ADTCWT reliably extracts the directional features in images. The proposed work concentrates on both binary classification as well as multiclass classification of DR. The system is evaluated across various classifiers such as Support Vector Machine (SVM), Random Forest, Random Tree, J48 classifiers by giving input image features extracted from the MESSIDOR, KAGGLE and DIARETDB0 databases. The performances of the classifiers are analyzed by comparing specificity, precision, recall, False Positive Rate (FPR) and accuracy values for each classifier. The evaluation results show that by applying the proposed feature extraction method, Random Forest outperforms all the other classifiers with an average accuracy of 99.7% and 99.82% for binary and multiclass classification respectively. INDEX TERMS DR binary classification, DR multiclass classification, retinal fundus images, HARALICK, ADTCWT, 10-fold cross validation. S. GAYATHRI received the B.Tech. degree in electronics and communication engineering from the Vivekandha College of Engineering, under Anna university, Chennai, in 2012, and the M.Tech. degree in advanced communication and information systems from the Rajiv Gandhi Institute of Technology, under Mahatma Gandhi University, Kottayam, in 2014. She is currently pursuing the Ph.D. degree in biomedical image processing with the National Institute of Technology at Tiruchirappalli, Tiruchirappalli, India. Her research interests include bio medical signal and image processing, machine learning, and deep learning.
The introduction of Optical Coherence Tomography (OCT) in ophthalmology has resulted in significant progress in the early detection of glaucoma. Traditional approaches to identifying retinal diseases comprise an analysis of medical history and manual assessment of retinal images. Manual diagnosis is time-consuming and requires considerable human expertise, without which, errors could be costly to human sight. The use of artificial intelligence such as machine learning techniques in image analysis has been gaining ground in recent years for accurate, fast and cost-effective diagnosis from retinal images. This work proposes a Directed Acyclic Graph (DAG) network that combines Depthwise Convolution (DC) to decisively recognize early-stage retinal glaucoma from OCT images. The proposed method leverages the benefits of both depthwise convolution and DAG. The Convolutional Neural Network (CNN) information obtained in the proposed architecture is processed as per the partial order over the nodes. The Grad-CAM method is adopted to quantify and visualize normal and glaucomatous OCT heatmaps to improve diagnostic interpretability. The experiments were performed on LFH_Glaucoma dataset composed of 1105 glaucoma and 1049 healthy OCT scans. The proposed faster hybrid Depthwise-Directed Acyclic Graph Network (D-DAGNet) achieved an accuracy of 0.9995, precision of 0.9989, recall of 1.0, F1-score of 0.9994 and AUC of 0.9995 with only 0.0047 M learnable parameters. Hybrid D-DAGNet enhances network training efficacy and significantly reduces learnable parameters required for identification of the features of interest. The proposed network overcomes the problems of overfitting and performance degradation due to accretion of layers in the deep network, and is thus useful for real-time identification of glaucoma features from retinal OCT images.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.