Diagnosis of Diabetic Retinopathy Utilizing Computer-Aided Diagnosis System
DOI:
https://doi.org/10.47672/ajce.580Keywords:
Computer-Aided Diagnosis, CAD, Diabetic Retinopathy, Fundus imaging, Image processing.Abstract
Purpose: Diabetes is considered one of most diseases spread among people; blindness is considered the most resulted effect. Diabetes can damage the retinal blood vessels and cause severe problems to the eyes, which may end with sight loss. Such medical condition is known as "diabetic retinopathy" (DR). In such a diagnosis, the retinal microvascular go through several stages of change threat. In the early stages of the DR, detecting the formation that happened to the retinal blood vessels helps prevent the disease's dangerous effects. Therefore, producing a method to diagnose the disease in the early stages is helpful. So, this work aimed to develop a system of detecting and classifying the retina formation, trying to avoid relevant effects.
Methodology: The current method depends on vascular edges map extracted from images of retina captured by a fundus camera. Such a map been utilized to extract quantitative texture features. The system tested two independent groups of the region of interest in normal and abnormal images. The two sets were extracted from ground truth images of the 89 fundus images. Fundus images were annotated images from the Standard Diabetic Retinopathy Database (DIARETDB1).
Findings: The system provided an accuracy of 71% with a sensitivity of 75%.
Recommendation: The current work may open an opportunity for improvement for future work. Other methods may be reached to raise the accuracy and the sensitivity of the system. Besides, the current system may be tested on a larger sample size to study such effects. Finally, the ease of the current method makes it faster in adoption in the appropriate diagnosis especially in the early stages.
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References
WHO, "Diabetes: key facts," vol. 2018, ed: world health organisation, 2018.
MAYO-CLINIC. "Diabetes." Mayo Foundation for Medical Education and Research (MFMER). https://www.mayoclinic.org/diseases-conditions/diabetes/symptoms-causes/syc-20371444?p=1 (accessed 10-Nov-2018, 2018).
WHO, "Global report on diabetes," World Health Organization; 1 edition (May 31, 2016), world health organisation, 9789241565257, 2016. [Online]. Available: http://www.who.int/iris/handle/10665/204871
K. Viswanath and D. D. M. McGavin, "Diabetic retinopathy: clinical findings and management," Community eye health, vol. 16, no. 46, pp. 21-24, 2003.
Y. Liu et al., "Prevalence of diabetic retinopathy among 13473 patients with diabetes mellitus in China: a cross-sectional epidemiological survey in six provinces," BMJ Open, 10.1136/bmjopen-2016-013199 vol. 7, no. 1, 2017.
Y. Zheng, M. He, and N. Congdon, "The worldwide epidemic of diabetic retinopathy," Indian journal of ophthalmology, vol. 60, no. 5, pp. 428-431, Sep-Oct 2012, doi: 10.4103/0301-4738.100542.
S. H. Alzahrani et al., "Awareness of diabetic retinopathy among people with diabetes in Jeddah, Saudi Arabia," Therapeutic advances in endocrinology and metabolism, vol. 9, no. 4, pp. 103-112, 2018, doi: 10.1177/2042018818758621.
A. Sopharak, B. Uyyanonvara, S. Barman, and T. H. Williamson, "Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods," Computerized Medical Imaging and Graphics, vol. 32, no. 8, pp. 720-727, 2008/12/01/ 2008, doi: https://doi.org/10.1016/j.compmedimag.2008.08.009.
L. B. Frazao, N. Theera-Umpon, and S. Auephanwiriyakul, "Diagnosis of diabetic retinopathy based on holistic texture and local retinal features," Information Sciences, vol. 475, pp. 44-66, 2019/02/01/ 2019, doi: https://doi.org/10.1016/j.ins.2018.09.064.
V. Gulshan, L. Peng, M. Coram, and et al., "Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs," JAMA, vol. 316, no. 22, pp. 2402-2410, 2016, doi: 10.1001/jama.2016.17216.
NEI. "NEI photos and images." National Eye Institute (NEI). https://nei.nih.gov/photo (accessed 16-11-2018, 2018).
C. E. Willoughby, D. Ponzin, S. Ferrari, A. Lobo, K. Landau, and Y. Omidi, "Anatomy and physiology of the human eye: effects of mucopolysaccharidoses disease on structure and function - a review," Clinical & Experimental Ophthalmology, vol. 38, no. s1, pp. 2-11, 2010, doi: 10.1111/j.1442-9071.2010.02363.x.
O. Gary Heiting. "The Retina: Where Vision Begins." AAV Media, LLC. © 2000-2018 AAV Media, LLC. https://www.allaboutvision.com/resources/retina.htm (accessed 16-11-2018.
Harvard, "definition of diabetic retinopathy," in Medical Dictionary of Health Terms, H. M. School, Ed., ed, 2011.
B. G. Khandare S, Gaffar S, Upadhyay A,, "AUTOMATIC DETECTION OF BLOOD VESSELS AND CLASSIFICATION OF RETINAL IMAGE INTO DIFFERENT STAGES OF DIABETIC RETINOPATHY," 2018, 2395-0056. [Online]. Available: https://www.irjet.net/volume5-issue4.
R. Adalarasan and R. Malathi, "Automatic Detection of Blood Vessels in Digital Retinal Images using Soft Computing Technique," Materials Today: Proceedings, vol. 5, no. 1, Part 1, pp. 1950-1959, 2018/01/01/ 2018, doi: https://doi.org/10.1016/j.matpr.2017.11.298.
D. S. S. Raja and S. Vasuki, "Automatic detection of blood vessels in retinal images for diabetic retinopathy diagnosis," Computational and mathematical methods in medicine, vol. 2015, pp. 419279-419279, 2015, doi: 10.1155/2015/419279.
R. Bala and D. D. Kumar, "Classification Using ANN : A Review," 2017.
K. Akyol, a. Bayir, B. En, and H. B. Akmak, "Detection of Hard Exudates in Retinal Fundus Images based on Important Features Obtained from Local Image Descriptors," Journal of Computer Sciences and Applications, vol. 4, no. 3, pp. 59-66, 2016/11/29 2016, doi: 10.12691/jcsa-4-3-2.
S. S. A. Hassan, D. B. L. Bong, and M. Premsenthil, "Detection of neovascularization in diabetic retinopathy," Journal of digital imaging, vol. 25, no. 3, pp. 437-444, 2012, doi: 10.1007/s10278-011-9418-6.
Z. Abduh, M. A. Wahed, and Y. M. Kadah, "Robust Computer-Aided Detection of Pulmonary Nodules from Chest Computed Tomography," Journal of Medical Imaging and Health Informatics, vol. 6, no. 3, pp. 693-699, // 2016, doi: 10.1166/jmihi.2016.1731.
J. Staal, M. D. Abramoff, M. Niemeijer, M. A. Viergever, and B. v. Ginneken, "Ridge-based vessel segmentation in color images of the retina," IEEE Transactions on Medical Imaging, vol. 23, no. 4, pp. 501-509, 2004, doi: 10.1109/TMI.2004.825627.
MESSIDOR. Methods to evaluate segmentation and indexing techniques in the field of retinal ophthalmology. [Online] Available: http://www.adcis.net/en/Download-Third-Party/Messidor.html
T. Kauppi et al., DIARETDB1 diabetic retinopathy database and evaluation protocol. 2007.
B. B, A STUDY ON THE IMPORTANCE OF IMAGE PROCESSING AND ITS APLLICATIONS. 2014, pp. 155-160.
P. M. Ingale, "The importance of Digital Image Processing and its applications," International Journal of Scientific Research in Computer Science and Engineering, vol. 06, no. 01, pp. 31-32, 2018.
N. Ahmed and W. Ahmed, The Application of Image Enhancement on Color and Grayscale Images. 2019.
S. Saini, B. Kasliwal, and S. Bhatia, "Comparative Study Of Image Edge Detection Algorithms," CoRR, vol. abs/1311.4963, / 2013.
G. Yang and F. Xu, "Research and analysis of Image edge detection algorithm Based on the MATLAB," Procedia Engineering, vol. 15, pp. 1313-1318, 2011/01/01/ 2011, doi: https://doi.org/10.1016/j.proeng.2011.08.243.
S. Katiyar and A. P V, Comparative analysis of common edge detection techniques in context of object extraction. 2012, pp. 68-78.
L. Ding and A. Goshtasby, "On the Canny edge detector," Pattern Recognition, vol. 34, no. 3, pp. 721-725, 2001/03/01/ 2001, doi: https://doi.org/10.1016/S0031-3203(00)00023-6.
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