Detection of severity level of diabetic retinopathy using Bag of features model. Issue 5 (10th July 2019)

• Record Type: Journal Article
• Authors: Leeza, Mona; Farooq, Humera
• Is Part Of: IET computer vision. Volume 13:Issue 5(2019)
• Page Start: 523
• Page End: 530
• Publication Date: 2019-07-10
• Subjects: eye -- patient diagnosis -- image representation -- medical image processing -- feature extraction -- image segmentation -- support vector machines -- image classification -- biomedical optical imaging -- learning (artificial intelligence) -- diseases; features model -- uncontrolled diabetes -- early detection -- diabetic patients -- severity level -- post-processing methods -- severity detection -- dictionary-based approach -- post-processing steps -- pathological explicit image representation -- visual features -- descriptive features -- robust features algorithm -- histogram -- nonproliferative diabetic retinopathy -- proliferative diabetic retinopathy; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2018.5263
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : Diabetic retinopathy is a vascular disease caused by uncontrolled diabetes. Its early detection can save diabetic patients from blindness. However, the detection of its severity level is a challenge for ophthalmologists since last few decades. Several efforts have been made for the identification of its limited stages by using pre‐ and post‐processing methods, which require extensive domain knowledge. This study proposes an improved automated system for severity detection of diabetic retinopathy which is a dictionary‐based approach and does not include pre‐ and post‐processing steps. This approach integrates pathological explicit image representation into a learning outline. To create the dictionary of visual features, points of interest are detected to compute the descriptive features from retinal images through speed up robust features algorithm and histogram of oriented gradients. These features are clustered to generate a dictionary, then coding and pooling are applied for compact representation of features. Radial basis kernel support vector machine and neural network are used to classify the images into five classes namely normal, mild, moderate, severe non‐proliferative diabetic retinopathy, and proliferative diabetic retinopathy. The proposed system exhibits improved results of 95.92% sensitivity and 98.90% specificity in relation to the reported state of the art methods.