Abstract
Diabetic retinopathy (DR) is a complication of diabetes mellitus that damages the blood vessels in the retina. DR is considered a serious vision-threatening impediment that most diabetic subjects are at risk of developing. Effective automatic detection of DR is challenging. Feature extraction plays an important role in the effective classification of disease. Here we focus on a feature extraction technique that combines two feature extractors, speeded up robust features and binary robust invariant scalable keypoints, to extract the relevant features from retinal fundus images. The selection of top-ranked features using the MR-MR (maximum relevance-minimum redundancy) feature selection and ranking method enhances the efficiency of classification. The system is evaluated across various classifiers, such as support vector machine, Adaboost, Naive Bayes, Random Forest, and multi-layer perception (MLP) when giving input image features extracted from standard datasets (IDRiD, MESSIDOR, and DIARETDB0). The performances of the classifiers were analyzed by comparing their specificity, precision, recall, false positive rate, and accuracy values. We found that when the proposed feature extraction and selection technique is used together with MLP outperforms all the other classifiers for all datasets in binary and multiclass classification.
Similar content being viewed by others
References
Cheung N, Wang JJ, Klein R, Couper DJ, Sharrett AR, Wong TY (2007) Diabetic retinopathy and the risk of coronary heart disease. Diabetes Care 30(7):1742–1746. https://doi.org/10.2337/dc07-0264
Zachariah S, Wykes W, Yorston D (2015) Grading diabetic retinopathy (dr) using the scottish grading protocol. Commun Eye Health 28:72–73
Abramoff MD, Garvin MK, Sonka M (2010) Retinal imaging and image analysis. IEEE Rev Biomed Eng 3:169–208. https://doi.org/10.1109/RBME.2010.2084567
Ali R, Usman Akram M (2018) Analysing vascular structure to determine intra retinal microvascular abnormalities (IRMA), pp 49–52. https://doi.org/10.1109/CIBEC.2018.8641825
Jemshi KM, Gopi VP, Issac Niwas S (2018) Development of an efficient algorithm for the detection of macular edema from optical coherence tomography images. Int J Comput Assist Radiol Surg 13(9):1369–1377. https://doi.org/10.1007/s11548-018-1795-6
Sreejini K, Govindan V (2019) Retrieval of pathological retina images using bag of visual words and plsa model. Int J Eng Sci Technol 22:777–785. https://doi.org/10.1016/j.jestch.2019.02.002
Tareen SAK, Saleem Z (2018) A comparative analysis of sift, surf, kaze, akaze, orb, and brisk. In: International conference on computing, mathematics and engineering technologies (iCoMET), pp 1–10. https://doi.org/10.1109/ICOMET.2018.8346440
Kamil R, Al-Saedi K, Al-Azawi R (2018) An accurate system to measure the diabetic retinopathy using svm classifier. Ciência e Técnica Vitivinícola 33:135–139
Lupascu CA, Tegolo D, Trucco E (2010) FABC: retinal vessel segmentation using adaboost. IEEE Trans Inf Technol Biomed 14(5):1267–1274. https://doi.org/10.1109/TITB.2010.2052282
Kausu T, Gopi VP, Wahid KA, Doma W, Niwas SI (2018) Combination of clinical and multiresolution features for glaucoma detection and its classification using fundus images. Biocybern Biomed Eng 38(2):329–341. https://doi.org/10.1016/j.bbe.2018.02.003
Abdulmunem M, Fatoohi Z (2018) Propose retina identification system based on the combination of surf detector and brisk descriptor. Iraqi J Sci 59(2B):946–955
Akyol K, BAYIR S, Sen B (2017) A decision support system for early-stage diabetic retinopathy lesions. Int J Adv Comput Sci Appl 8:369–379. https://doi.org/10.14569/IJACSA.2017.081249
Naga Sai Prasad VG, Ratna B, Rajesh V (2018) Feature extraction based retinal image analysis for bright lesion classification in fundus image. Biomed Res 29:3648–3653. https://doi.org/10.4066/biomedicalresearch.29-16-2170
de la Calleja J, Tecuapetla L, Auxilio Medina M, Bárcenas E, Urbina Nájera AB (2014) LBP and machine learning for diabetic retinopathy detection. Int Conf Intell Data Eng Autom Learn 8669:110–117. https://doi.org/10.1007/978-3-319-10840-7_14
Issac Niwas S, Lin W, Kwoh CK, Kuo CJ, Sng CC, Aquino MC, Chew PTK (2016) Cross-examination for angle-closure glaucoma feature detection. IEEE J Biomed Health Inform 20(1):343–354. https://doi.org/10.1109/JBHI.2014.2387207
Sidibé D, Sadek I, Mériaudeau F (2015) Discrimination of retinal images containing bright lesions using sparse coded features and svm. Comput Biol Med 62:175–184. https://doi.org/10.1016/j.compbiomed.2015.04.026
Jelinek HF, Pires R, Padilha R, Goldenstein S, Wainer J, Bossomaier T, Rocha A (2012) Data fusion for multi-lesion diabetic retinopathy detection. In: 25th IEEE international symposium on computer-based medical systems (CBMS), pp 1–4. https://doi.org/10.1109/CBMS.2012.6266342
Panchal P, Bhojani R, Panchal T (2016) An algorithm for retinal feature extraction using hybrid approach. Procedia Comput Sci 79:61–68. https://doi.org/10.1016/j.procs.2016.03.009. Proceedings of international conference on communication, computing and virtualization (ICCCV) 2016
Li H, Chutatape O (2004) Automated feature extraction in color retinal images by a model based approach. IEEE Trans Biomed Eng 51(2):246–254. https://doi.org/10.1109/TBME.2003.820400
Gopi VP, Anjali MS, Niwas SI (2017) Pca-based localization approach for segmentation of optic disc. Int J Comput Assist Radiol Surg 12(12):2195–2204. https://doi.org/10.1007/s11548-017-1670-x
Sudha V, Karthikeyan C (2018) Analysis of diabetic retinopathy using naive bayes classifier technique. Int J Eng Technol 7:440–442. https://doi.org/10.14419/ijet.v7i2.21.12462
Rahim SS, Palade V, Shuttleworth J, Jayne C (2016) Automatic screening and classification of diabetic retinopathy and maculopathy using fuzzy image processing. Brain Inform 3(4):249–267. https://doi.org/10.1007/s40708-016-0045-3
Bay H, Tuytelaars T, Van Gool L (2006) Surf: speeded up robust features. In: Leonardis A, Bischof H, Pinz A (eds) Computer vision—ECCV 2006, vol 3951. Springer, Berlin, pp 404–417
Leutenegger S, Chli M, Siegwart RY (2011) Brisk: binary robust invariant scalable keypoints. In: 2011 international conference on computer vision, pp 2548–2555. https://doi.org/10.1109/ICCV.2011.6126542
Gularte A, Thomasi C, De Bem R, Adamatti D (2013) Performance evaluation of brisk algorithm on mobile devices. VISAPP 2013 Proc Int Conf Comput Vis Theory Appl 2:5–11
Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238. https://doi.org/10.1109/TPAMI.2005.159
Kandhasamy JP, Kadry Balamurali S, Ramasamy LK (2019) Diagnosis of diabetic retinopathy using multi level set segmentation algorithm with feature extraction using svm with selective features. Multimed Tools Appl. https://doi.org/10.1007/s11042-019-7485-8
Daqi G, Tao Z (2007) Support vector machine classifiers using RBF kernels with clustering-based centers and widths. In: 2007 international joint conference on neural networks, pp 2971–2976. https://doi.org/10.1109/IJCNN.2007.4371433
Wang R (2012) Adaboost for feature selection, classification and its relation with svm, a review. Phys Procedia 25:800–807. https://doi.org/10.1016/j.phpro.2012.03.160. International conference on solid state devices and materials science, macao
Schapire RE (2013) Explaining AdaBoost. Springer, Berlin, pp 37–52. https://doi.org/10.1007/978-3-642-41136-6_5
Roychowdhury A, Banerjee S (2018) Random forests in the classification of diabetic retinopathy retinal images. In: Bhattacharyya S, Gandhi T, Sharma K, Dutta P (eds) Advanced computational and communication paradigms, vol 475. Springer, Singapore, pp 168–176. https://doi.org/10.1007/978-981-10-8240-5_19
Breiman L (2001a) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324
Breiman L (2001b) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324
Huang G-B, Chen Y-Q, Babri HA (2000) Classification ability of single hidden layer feed forward neural networks. IEEE Trans Neural Netw 11(3):799–801. https://doi.org/10.1109/72.846750
Saifuddin H, Vijayalakshmi H (2016) Prediction of diabetic retinopathy using multi layer perceptron. Int J Adv Res 4:658–664. https://doi.org/10.21474/IJAR01/714
Yadav S, Shukla S (2016) Analysis of k-fold cross-validation over hold-out validation on colossal datasets for quality classification. In: 2016 IEEE 6th international conference on advanced computing (IACC), pp 78–83. https://doi.org/10.1109/IACC.2016.25
Visa S, Ramsay B, Ralescu A, Knaap E (2011) Confusion matrix-based feature selection. CEUR Workshop Proc 710:120–127
Porwal P, Pachade S, Kamble R, Kokare M, Deshmukh G, Sahasrabuddhe V, Meriaudeau F (2018) Indian diabetic retinopathy image dataset (IDRiD): a database for diabetic retinopathy screening research. Data 3:1–8
Decencière E, Zhang X, Cazuguel G, Lay B, Cochener B, Trone C, Gain P, Ordonez R, Massin P, Erginay A, Charton B, Klein JC (2014) Feedback on a publicly distributed database: the messidor database. Image Anal Stereol 33(3):231–234. https://doi.org/10.5566/ias.1155
Kalesnykiene V, kristian Kamarainen J, Lensu L, Sorri I, Uusitalo H, Kälviäinen H, Pietilä J (2007) DIARETDB0: Evaluation database and methodology for diabetic retinopathy algorithms
He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 770–778
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval
For this type of study, formal consent is not required.
Informed consent
This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Gayathri, S., Gopi, V.P. & Palanisamy, P. Automated classification of diabetic retinopathy through reliable feature selection. Phys Eng Sci Med 43, 927–945 (2020). https://doi.org/10.1007/s13246-020-00890-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13246-020-00890-3