Abstract
The Diabetic Retinopathy (DR) is a critical vascular disorder that can cause a permanent blindness. Thus, the early recognition and the treatment are required to avoid major vision loss. Nowadays manual screening is done however, they are very incompetent to large image database of patients and most importantly they are very time consuming. Besides, it required skilled professionals for the diagnosis. Automatic DR diagnosis systems can be as an optional method to the manual methods as they can significantly reduce the manual screening process labor. Screening conducted over a larger population can become effective if the system can distinguish between normal and abnormal cases, as a replacement for the manual examination of all images. Hence, the development of an Automated Diabetic Retinopathy detection systems has been recognized in the current times. This study has successfully developed an automated detection system for proliferative diabetic retinopathy symptoms using an artificial neural network with two types of feature used; mean of pixel and area of the pixel. The highest accuracy of this system is 90% with 30 hidden neurons in the neural network trained for all features. The results clearly show that the proposed method is effective for detection of Diabetic Retinopathy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
International Diabetes Federation and World Health Organization.: The Western Pacific Declaration on Diabetes, Kuala Lumpur, June 2000. WHO, Manila (2000)
Van Grinsven, M., van Ginneken, B., Sanchez, C.: Computer-Aided Diagnosis of Diabetic Retinopathy (CAD-DR). Diagnostic Image Analysis Group [Online] (2018). Diagnijmegen.nl. Available at: http://diagnijmegen.nl/index.php/ComputerAided_Diagnosis_of_Diabetic_Retinopathy_(CAD-DR. Accessed 27 May 2018
Associates, A.: Diabetic Retinopathy. Arleo Eye Associates [online] (2018). Available at: http://arleoeye.com/services/common-eyedisorders/diabetic-retinopathy/. Accessed 24 May 2018
Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M., Goldbaum, M.: Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans. Med. Imaging 8(3), 263–269 (1989)
Grisan, E., Pesce, A., Giani, A., Foracchia, M., Ruggeri, A.: A new tracking system for the robust extraction of retinal vessel structure. In: 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEMBS’04, vol. 1, pp. 1620–1623 (2004)
Tyler, C.: A Novel Retinal Blood Vessel Segmentation Algorithm for Fundus Images. http://www.mathworks.com/matlabcentral/fileexchange/50839. MATLAB Central File Exchange. Last accessed 01 Sept 2018
Nafeela, J.N.: Detecting and segmenting digital retinal blood vessels using neural network. Int. J. Eng. Res. Rev. 2(1), 36–43 (2014)
Staal, J., Abrà moff, M.D., Niemeijer, M., Viergever, M.A., Van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501–509 (2004)
Budai, A., Bock, R., Maier, A., Hornegger, J., Michelson, G.: Robust vessel segmentation in fundus images. Int. J. Biomed. Imaging 2013 (2013)
Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Prentice Hall (2010). ISBN 9780136042594
Mohri, M., Rostamizadeh, A., Talwalkar, A.: Foundations of Machine Learning. The MIT Press (2012). ISBN 9780262018258
Dizdaro, B., Ataer-Cansizoglu, E., Kalpathy-Cramer, J., Keck, K., Chiang, M.F., Erdogmus, D.: Level sets for retinal vasculature segmentation using seeds from ridges and edges from phase maps. In: 2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1–6 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Shaharum, S.M., Hashim, N.H., Abu Talip @ Yusof, N., Abdul Karim, M.S., Mohd Faudzi, A.A. (2019). Automatic Detection of Diabetic Retinopathy Retinal Images Using Artificial Neural Network. In: Md Zain, Z., et al. Proceedings of the 10th National Technical Seminar on Underwater System Technology 2018 . Lecture Notes in Electrical Engineering, vol 538. Springer, Singapore. https://doi.org/10.1007/978-981-13-3708-6_43
Download citation
DOI: https://doi.org/10.1007/978-981-13-3708-6_43
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-3707-9
Online ISBN: 978-981-13-3708-6
eBook Packages: EngineeringEngineering (R0)