Automated detection of diabetic retinopathy in fundus images using fused features

• Published in: Australasian physical & engineering sciences in medicine Vol. 43; no. 4; pp. 1253 - 1264
• Main Authors: Bibi, Iqra, Mir, Junaid, Raja, Gulistan
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.12.2020; Springer Nature B.V
• Subjects: Biological and Medical Physics; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Biophysics; Blood vessels; Classifiers; Diabetes; Diabetic retinopathy; Histograms; Image acquisition; Image segmentation; Medical and Radiation Physics; Model accuracy; Scientific Paper; Sensitivity; Support vector machines; Diabetic retinopathy; Retinal image; Fundus images; Texture features; CAD systems
• ISSN: 2662-4729; 0158-9938; 2662-4737; 1879-5447
• DOI: 10.1007/s13246-020-00929-5

Diabetic retinopathy (DR) is one of the severe eye conditions due to diabetes complication which can lead to vision loss if left untreated. In this paper, a computationally simple, yet very effective, DR detection method is proposed. First, a segmentation independent two-stage preprocessing based technique is proposed which can effectively extract DR pathognomonic signs; both bright and red lesions, and blood vessels from the eye fundus image. Then, the performance of Local Binary Patterns (LBP), Local Ternary Patterns (LTP), Dense Scale-Invariant Feature Transform (DSIFT) and Histogram of Oriented Gradients (HOG) as a feature descriptor for fundus images, is thoroughly analyzed. SVM kernel-based classifiers are trained and tested, using a 5-fold cross-validation scheme, on both newly acquired fundus image database from the local hospital and combined database created from the open-sourced available databases. The classification accuracy of 96.6% with 0.964 sensitivity and 0.969 specificity is achieved using a Cubic SVM classifier with LBP and LTP fused features for the local database. More importantly, in out-of-sample testing on the combined database, the model gives an accuracy of 95.21% with a sensitivity of 0.970 and specificity of 0.932. This indicates the proposed model is very well-fitted and generalized which is further corroborated by the presented train-test curves.