Abstract
Detection of Diabetic Retinopathy and Age related Macular Degeneration is a challenge for the ophthalmologists as the abnormalities are merely visible at the early stage. Early detection of these diseases can prevent permanent vision loss. Handling a large amount of retinal images and detection of abnormalities due to these diseases is laborious as well as time consuming. In this research work, an algorithm is developed for identifying the abnormal objects in retina, if any with a machine learning technique using Naïve Bayes classification is proposed. A training set is generated with features of abnormalities present in retinal image and the type of disease the retina is suffering from. The Naïve Bayes classifier helps to predict the disease for each retinal image after gathering the knowledge from training set. The correctness of prediction is calculated to measure the efficiency of the classifier. The system achieves 97.014% of accuracy on an average.
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References
Akram MU, Khalid S, Tariq A, Khan SA, Azam F (2014) Detection and classification of retinal lesions for grading of diabetic retinopathy. Comput Biol Med 45:161–171
Kose C, Ikibas C (2011) Statistical techniques for detection of optic disc and macula and parameters measurement in retinal fundus images. J Med Biol Eng 31(6):395–404
Sopharak A, Nwe KT, Moe YA, Dailey MN, Uyyanonvara B (2008) Automatic exudate detection with a naive Bayes classifier. In: The international conference on embedded systems and intelligent technology, pp 139–142, Grand Mercure Fortune Hotel, Bangkok, Thailand, 27–29 Feb 2008
Niemeijer M, Ginneken BV, Russell SR, Suttorp-Schulten MSA, Abramoff MD (2007) Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis. IOVS 48(5):2260–2267
Youssif AAHAR, Ghalwash AZ, Ghoneim AASAR (2008) Optic disc detection from normalized digital fundus images by means of a vessels’ direction matched filter. IEEE Trans Med Imaging 27(1):11–18
Jassim FA, Altaani FH (2001) Hybridization of Otsu method and median filter for color image segmentation. Int J Soft Comput Eng 3(2):69–74
Abbadi NKE, Saadi EHA (2013) Automatic detection of exudates in retinal images. Int J Soft Comput Eng 10(2):237–242
Roy Chowdhury A, Saha R, Banerjee S (2015) Detection of different types of diabetic retinopathy and age related macular degeneration. In: Computer science and application (CSA) 2015 Proceedings, pp 71–76
Banerjee S, Roy Chowdhury A (2015) Case based reasoning in the detection of retinal abnormalities using decision trees. International conference on information and communication technologies (ICICT 2014). Proc Comput Sci 46:402–408
Larsen M, Godt J, Larsen N, Andersen HL, Sjolie AK, Agardh E, Kalm H, Grunkin M, Owens DR (2003) Automated detection of fundus photographic red lesions in diabetic retinopathy. IOVS 44(2):761–766
Walter T, Klein JC, Massin P, Erginay A (2002) A contribution of image processing to the diagnosis of diabetic retinopathy—detection of exudates in color fundus images of the human retina. IEEE Trans Med Imaging 21(10):1236–1243
Sinthanayothin C, Boyce JF, Williamson TH, Cook HL, Mensah E, Lal S, Usher D (2002) Automated detection of diabetic retinopathy on digital fundus images. Diabet Med 19:105–112
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Roy Chowdhury, A., Banerjee, S. Towards an automated approach to the detection of retinal abnormalities. CSIT 5, 71–78 (2017). https://doi.org/10.1007/s40012-016-0132-x
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DOI: https://doi.org/10.1007/s40012-016-0132-x