Non-proliferative Diabetes Retinopathy Images Classification Task in Healthcare

Sabibullah Mohamed Hanifa*
*Associate Professor & Dean, Department of Computer Science, Sudharsan College of Arts & Science, Pudukkottai, Tamilnadu, India.
Periodicity:June - August'2016
DOI : https://doi.org/10.26634/jpr.3.2.8263

Abstract

Diabetes Mellitus (DM) is a metabolic disorder gets great high impact on human life in recent years. The WHO has estimated that the number of diabetes in the world by 2025 may reach up to 60 million and India's contribution would be 30 million. Recent report says that one fifth of Asian countries, most lives are lost due to non-communicable diseases like cardiovascular disease, Cancer and Diabetes. Preventing the disease of diabetes is an ongoing area of interest to the healthcare community. Long term complications of DM patients' includes; Retinopathy (Disease affecting human eye/ retina), Neuropathy (Neuro-deficit problems/Nerve Damages), Nephropathy (Chronic kidney failure), Gastropathy, Cardiovascular disease (Heart attack), Cerebrovascular disease (Parkinson's disease, Stroke), Foot ulcers and Premature death. Diabetes Retinopathy (DR) is retinopathy, it only affects people who have had diabetes for a long time period and can result in blindness/loss of vision. The sight-threatening stages of DR can be broadly classified as Non-Proliferative Diabetic Retinopathy (NPDR), and Proliferative Diabetic Retinopathy (PDR). This study presents the DR patients' prevention and detecting the retinal images in early stages, can be treated more easily and clinically. A study by Indian Council for Medical Research (ICMR's) INDIAB (India Diabetes) confirmed that one out of 10 people in Tamilnadu are diabetic, and every two adults in a group of 25 are in the pre-diabetic stage. From this study, it is mandatory on clinical research to screen the diabetes patients in the line of retinopathy. MATLAB – classifier predicts the high accuracy level of retinal images classification out of three stages in non-proliferative retinopathy.

Keywords

Diabetic Mellitus, Retinopathy, Classifier, Non-proliferative, Retinal Images, Transformation, Healthcare

How to Cite this Article?

Hanifa, S. M. (2016). Non-proliferative Diabetes Retinopathy Images Classification Task in Healthcare. i-manager’s Journal on Pattern Recognition, 3(2), 7-16. https://doi.org/10.26634/jpr.3.2.8263

References

[1]. Seema Abhijeet Kaveeshwar, and Jon Cornwall, (2014). “The Current State of Diabetes Mellitus in India”. Australas Med. Jr., Vol. 7, No. 1, pp. 45-48.
[2]. Kounteya Sinha, (2011). India’s Diabetes Burden to Cross 100 Million by 2030. The Times of India.
[3]. A.D. Fleming, K.A. Goatman, and J.A. Olson, (2010). “The role of exudates and hemorrhage detection in automated grading of diabetic retinopathy”. British Journal of Ophthalmology, Vol. 94, No. 6, pp. 706-711.
[4]. Hipwell J.H, Strachan F, Olson J.A, McHardy K.C, Sharp P.F, and Forrester J.V, (2000). “Automated detection of micro-aneurysms in digital red-free photographs: A diabetic retinopathy screening tool”. Diabet Med, Vol. 17, pp. 588–594.
[5]. Lee S, Lee E, Kingsley R, Wang Y, Russell D, and Klein R, (2001). “Comparison of diagnosis of early retinal lesions of diabetic retinopathy between a computer and human experts”. Arch. Ophthalmol, Vol. 119, pp. 509–515.
[6]. Sinthanayothin C, Boyce J.F, Williamson T.H, Cook H.L, Mensah E, and Lal S, (2002). “Automated detection of diabetic retinopathy on digital fundus image”. Journal Diabet Med, Vol. 19, No. 105–112.
[7]. Niemeijer M, Van Ginneken B, Staal J, Suttorp- Schulten M.S, and Abramoff M.D, (2005). “Automatic detection of red lesions in digital color fundus photographs”. IEEE Trans. Med. Imag., Vol. 24, pp. 584–592.
[8]. Usher D, Dumskyj M, Himaga M, Williamson T.H, Nussey S, and Boyce J, (2004). “Automated detection of diabetic retinopathy in digital retinal images: A tool for diabetic retinopathy screening”. Diabet Med, Vol. 21, pp. 84–90.
[9] Huiqi, Li., and Chutatape, O, (2003). “A model-based approach for automated feature extraction in fundus images”. International Conference on Computer Vision (ICCV), pp. 394-399.
[10] Goh K.G., Hsu, W., Li Lee, Wang, H. and Adris, (2001). An Automatic Diabetic Retinal Image Screening System. Medical DM&KD, Krzysztof, J.C., Editor. Physica-Verlag: Heidelberg, Germany, pp. 181-210.
[11]. Ege, B.M., Hejlese, O.K., Larsen, O.V., Moller, K., Jennings, B., Kerr, D., and Cavan, D.A., (2000). “Screening for diabeticretinopathy using computer based image analysis and statistical classification”. Comput. Meth. Programs Biomed., Vol. 62, pp. 165-175.
[12]. B. Kande, S.S. Tirumala, and P.V. Subbaiah, (2010). “Automatic detection of micro-aneur ysms and haemorrhages in digital fundus images”. Journal of Digital Imaging, pp. 430-437.
[13]. B.Kande, S.S. Tirumala, P.V. Subbaiah, and M.R. Tagore, (2009). “Detection of red lesions in digital fundus images”. In Proc. ISBI, pp. 558-561.
[14]. STARE: Structured Analysis of the Retina. [Online], Available: http://www.ces.c1emson.edu-ahoover/STARE
[15]. DIARETDB1: Diabetic Retinopathy Database and Evlauation Protocol. Retrieved from http://www2.it\ut .fi/projectlimageret/
[16]. L. Tang, M. Niemeijer and M. Abramoff, (2013). “Splat feature classification: Detection of the presence of  large retinal heamorrhages”. Proc. IEEE 8th Int. Symp. Biomed. Imaging (ISBI), pp. 681-684.
[17]. MESSIDOR: Methods to Evaluate Segmentation and Indexing Techniques in the Field of Retinal Ophthalmology. Techno-vision Project. Retrieved from http:// www. messidor.crihan.fr/
[18]. Bob Zang, Xiangqian Wu, Jane You, Qin Li, and FakhriKarray, (2010). “Detection of micro-aneurysms using Multi-scale correlation co-efficient”. Elsevier, Pattern Recognition, pp. 2237-2248.
[19]. ROC (Retinopathy Online Challenge). Retrieved from http://roc.healthcare.uiowa.edu
[20]. Akara Sopharak, Mathew N. Dailey, Bunyarit Uyyanonvara, Sarah Barman, Tom Williamson, and Yin Aye Moe, (2011). “Machine Learning approach to automatic Exudates detection in retinal images from diabetic patients”. Journal of Modern Optics, Vol. 57, No. 2, pp. 124-135.
[21]. C.I. Sanchez, R. Hornero, M.I. Lopez, and J. Poza, (2004). “Retinal Image Analysis to Detect and Quantify  Lesions Associated with Diabetic Retinopathy”. Proc. 26th IEEE Annual International Conference on Engineering in Medicine and Biology Society (EMBC), Vol. 3, pp. 1624–1627.
[22]. Sabibullah M, (2012). “Prognostic Neural Network model for diabetic risks prediction”. Proc. of IEEE International Conference on Emerging Trends in Science, Engineering and Technology, pp. 392-395.
[23]. Sabibullah M, Shanmugasundaram V, and Raja Priya K, (2013). “Diabetes Patient's Risk through Soft Computing Model”. International Journal of Emerging Trends & Tech. in Comp. Sci. (IJETTS), Vol. 2, No. 6, pp. 61- 65.
[24]. Sabibullah M, and Kashmir Raja S.V, (2010). “Prediction of stoke risk through stacked topology of ANN model”. International Journal of Advanced Research in Computer Science, Vol. 1, No. 4, pp. 170-177.
[25]. Sabibullah M, and Kashmir Raja S.V, (2010). “Stroke risk prediction through Non-linear Support Vector Classification Models”. International Journal of Advanced research in Computer Science, Vol. 1, No. 3, pp. 47-53.
[26]. Sabibullah M, and Kashmir Raja S.V, (2009). “A study on cerebrovascular disease risk factor prediction through fuzzy inference system”. International Journal of System Simulation, Vol. No. 1, pp. 15-23.
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