Abstract
The basic objective of the proposed work is to identify abnormalities caused by Diabetic Retinopathy in human retina. We classified the retinal images into two categories; normal retina and abnormal retina which contains some signs of Diabetic Retinopathy. In this work, we detect these problems using the algorithms: Support Vector Machine (SVM), k-Nearest Neighbour and Convolutional Neural Network (CNN) algorithm. These algorithms are used to build a model and their performances are compared with each other. The result is that the Support Vector Machine (SVM) gives the best accuracy of 96.6% with sensitivity and specificity of 0.66 and 0.95 respectively. Such type of model is very helpful in the early detection and treatment of Diabetic Retinopathy.
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References
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. Imag. 8(3), 263269 (1989)
Vallabha, D., Dorairaj, R., Namuduri, K., Thompson, H.: Automated detection and classification of vascular abnormalities in diabetic retinopathy. In: Proceedings of 13th IEEE Signals, Systems and Computers, vol. 2, pp. 1625–1629 (2004)
Sinthanayothin, C., Boyce, J., Williamson, T., Cook, H., Mensah, E., LaI, S., Usher, D.: Automated detection of diabetic retinopathy on digital fundus images. Diabet. Med. 19, 105–112 (2002)
Noronha, K., Nayak, J., Bhat, S.: Enhancement of retinal fundus image to highlight the features for detection of abnormal eyes. In: Proceedings of the IEEE Region10 Conference (TENCON2006) (2006)
Lay, B., Baudoin, C., Klein, J.-C.: Automatic detection of micro aneurysms in retinopathy fluoro-angiogram. Proc. SPIE 432, 165 (1983)
Ege, B.M., Hejlesen, O.K., Larsen, O.V., Moller, K., Jennings, B., Kerr, D., Cavan, D.A.: Screening for diabetic retinopathy using computer based image analysis and statistical classification. Comput. Meth. Programs Biomed. 62, 165–175 (2000)
Lee, S., Lee, E., Kingsley, R., Wang, Y., Russell, D., Klein, R., Warn, A.: Comparison of diagnosis of early retinal lesions of diabetic retinopathy between a computer and human experts. Arch. Ophthalmol. (2001)
Gardner, G., Keating, D., Williamson, T., Elliott, A.: Automated detection of diabetic retinopathy using an artificial neural network: a screening tool. Br. J. Ophthalmol. 86, 940–944 (1996)
Bezdek, J., Pal, M., Keller, J., Krisnapuram, R.: Fuzzy Model and Algorithms for Pattern Recognition and Image Processing. Kluwer Academic Press, London (1999)
Osareh, A., Mirmedhi, M., Thomas, B., Markham, R.: Automated identification of diabetic retinal exudates in digital color imaging. Br. J. Ophthalmol. 87, 1220–1223 (2003)
Acharya, U.R., Chua, K.C., Ng, E.Y.K., Wei, W., Chee, C.: Application of higher order spectra for the identification of diabetes retinopathy stages. J. Med. Syst. 32(6), 481–488 (2008)
Kahai, P., Namuduri, K.R., Thompson, H.: A decision support framework for automated screening of diabetic retinopathy. Int. J. Biomed. Imag. 2006, 18 (2006)
Wong, L.Y., Acharya, U.R., Venkatesh, Y.V., Chee, C., Lim, C.M., Ng, E.Y.K.: Identification of different stages of diabetic retinopathy using retinal optical images. Inform. Sci. 178(1), 106121 (2008)
Acharya, U.R., Lim, C.M., Ng, E.Y.K., Chee, C., Tamura, T.: Computer based detection of diabetes retinopathy stages using digital fundus images. J. Eng. Med. 223(H5), 545553 (2009)
Adarsh, P., Jeyakumari, D.: Multiclass SVM-based automated diagnosis of diabetic retinopathy. In: International Conference on Communication and Signal Processing, India (2013)
It.lut.fi.: Diaretdb1-standard diabetic retinopathy database (2018). http://www.it.lut.fi/project/imageret/diaretdb1/. Accessed 7 July 2018
Kauppi, T., Kalesnykiene, V., Kamarainen, J.-K., Lensu, L., Sorri, I., Raninen A., Voutilainen R., Uusitalo, H., Klviinen, H., Pietil, J.: DIARETDB1 diabetic retinopathy database and evaluation protocol, Technical report
Babu, N.R., Mohan, B.J.: Fault classification in power systems using EMD and SVM. Ain Shams Eng. J. (2015)
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Dandapat, S., Ghosh, S., Si, S., Datta, A. (2021). Analysis of Diabetic Retinopathy Abnormalities Detection Techniques. In: Bhattacharjee, D., Kole, D.K., Dey, N., Basu, S., Plewczynski, D. (eds) Proceedings of International Conference on Frontiers in Computing and Systems. Advances in Intelligent Systems and Computing, vol 1255. Springer, Singapore. https://doi.org/10.1007/978-981-15-7834-2_22
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DOI: https://doi.org/10.1007/978-981-15-7834-2_22
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