Abstract
In this paper, we proposed a hybrid automated detection system based least square support vector machine (LSSVM) and k-NN based weighted pre-processing for diagnosing of macular disease from the pattern electroretinography (PERG) signals. k-NN based weighted pre-processing is pre-processing method, which is firstly proposed by us. The proposed system consists of two parts: k-NN based weighted pre-processing used to weight the PERG signals and LSSVM classifier used to distinguish between healthy eye and diseased eye (macula diseases). The performance and efficiency of proposed system was conducted using classification accuracy and 10-fold cross validation. The results confirmed that a hybrid automated detection system based on the LSSVM and k-NN based weighted pre-processing has potential in detecting macular disease. The stated results show that proposed method could point out the ability of design of a new intelligent assistance diagnosis system.
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Quigley, H.A., Addicks, E.M., Green, W.R.: Optic nerve damage in human glaucoma. III. Quantitative correlation of nerve fiber loss and visual defect in glaucoma, ischemic neuropathy, papilledema and toxic neuropathy. Arch. Ophthalmol. 100, 135–146 (1982)
Quigley, H.A., Dunkelberger, G.R., Green, W.R.: Chronic human glaucoma causing selectively greater loss of large optic nerve fibers. Ophthalmology 95, 357–363 (1988)
Bobak, P., Bodis-Wollner, I., Harnois, C., et al.: Pattern electroretinograms and visual-evoked potentials in glaucoma and multiple sclerosis. Am. J. Ophthalmol. 96, 72–83 (1983)
Falsini, B., Colotto, A., Porciatti, V., et al.: Macular flicker- and pattern-ERGs are differently affected in ocular hypertension and glaucoma. Clin. Vis. Sci. 6, 423–429 (1991)
Graham, S.L., Wong, V.A.T., Drance, S.M., Mikelberg, F.S.: Pattern electroretinograms from hemifields in normal subjects and patients with glaucoma. Invest Ophthalmol. Vis. Sci. 35, 3347–3356 (1994)
O’Donaghue, E., Arden, G.B., O’Sullivan, F., et al.: The pattern electroretinogram in glaucoma and ocular hypertension. Br. J. Ophthalmol. 76, 387–394 (1992)
Pfeiffer, N., Tillmon, B., Bach, M.: Predictive value of the pattern electroretinogram in high-risk ocular hypertension. Invest Ophthalmol. Vis. Sci. 34, 1710–1715 (1993)
Porciatti, V., Falsini, B., Brunori, S., et al.: Pattern electroretinogram as a function of spatial frequency in ocular hypertension and early glaucoma. Doc. Ophthalmol. 65, 349–355 (1987)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Suykens, J.A.K., Vandewalle, J.: Least squares support vector machine classifiers. Neural Processing Letters 9(3), 293–300 (1999)
Tsujinishi, D., Abe, S.: Fuzzy least squares support vector machines for multi-class problems. Neural networks field 16, 785–792 (2003)
Kohavi, R., Provost, F.: Glossary of Terms. Editorial for the Special Issue on Applications of Machine Learning and the Knowledge Discovery Process 30(2-3) (1998)
Polat, K., Güneş, S.: A hybrid medical decision making system based on principles component analysis, k-NN based weighted pre-processing and adaptive neuro-fuzzy inference system. Digital Signal Processing 16(6), 913–921 (2006)
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Polat, K., Kara, S., Güven, A., Güneş, S. (2007). A Hybrid Automated Detection System Based on Least Square Support Vector Machine Classifier and k-NN Based Weighted Pre-processing for Diagnosing of Macular Disease. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71629-7_38
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DOI: https://doi.org/10.1007/978-3-540-71629-7_38
Publisher Name: Springer, Berlin, Heidelberg
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