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IJSTR >> Volume 5 - Issue 4, April 2016 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Radial Basis Function Neural Network Based Classifier For Diagnosing Of MCI/AD Using Multimodal Neuroimaging

[Full Text]



R.Ramya, S.P.Sivagnana Subramanian, ADNI



Image Registration, Feature Extraction, Radial basis function neural network, performance evaluation.



Neuroimaging has played a very important role in the diagnosis of brain degeneration disorders, such as Alzheimer’s disease (AD) and Mild Cognitive Impairment (MCI). To identify different stages of Alzheimer’s disease and efficient analysis system has been developed for magnetic resonance Imaging (MRI) and positron emission tomography (PET) Neuroimages using radial basis function neural network (RBFNN) classifier.Normal, MCI and AD identification by using RBFNN classifier. The proposed model performance was assessed based on three parameters such as sensitivity, specificity and accuracy.



[1] Carlos Cabral, Pedro M.Morgado, Durval Campos Costa, Margarida Silveira,“Predicting conversion from MCI to AD with FDG-PET brain images at different prodromal stages ”, Computers in Biology and Medicine,elseveir,Vol. 58,pp. 101–109,2015.

[2] Pedro M.Morgado, Margarida Silveira,“Minimal neighborhood redundancy maximal relevance: Application to the diagnosis of Alzheimer's disease ”, Neurocomputing,elseveir,Vol. 155,pp. 295–308,2014.

[3] Sidong Liua, Weidong Caia, Lingfeng Wena, David Dagan Fenga, Sonia Pujolb,Ron Kikinisb, Michael J. Fulhama,Stefan Eberla,“Multi-Channel neurodegenerative pattern analysis and its application in Alzheimer’s disease characterization ”, Computerized Medical Imaging and Graphics,elseveir,Vol. 38,pp. 436–444,2014.

[4] Ben Rabeh Amira, Benzarti faouzi, Amiri Hamid, Mouna Bouaziz,“Computer-assisted diagnosis of Alzheimer's disease”, International Image Processing Applications And Systems Conference, 2013.

[5] Biswajit Pathak, Debajyoti Barooah,“ Texture Analysis Based On The Gray-Level Co-Occurrence Matrix Considering Possible Orientations”, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol. 2, Issue 9, September 2013.

[6] Ahmad Ashari, Iman Paryudi and A Min Tjoa,“ Performance Comparison between Naïve Bayes, Decision Tree and k-Nearest Neighbor in Searching Alternative Design in an Energy Simulation Tool”, International Journal of Advanced Computer Science and Applications, Vol. 4, No. 11, pp. 33-39,2013.

[7] Eduardo Bicacro, Margarida Silveira, Jorge S. Marques and Durval C. Costa,“ 3d Brain Image-Based Diagnosis Of Alzheimer’s Disease:Bringing Medical Vision Into Feature Selection”, IEEE,pp. 134-138,2012.

[8] Qian Y, Gao X, Loomes M, Comley R, Barn B, Hui R, Tian z,“Content-based retrieval of 3D medical images”, International Conference on eHealth, Telemedicine, and Social Medicine (eTELEMED),Vol. 99,pp 7-12,2011.

[9] Pradipta Maji and Sankar K. Pal, “Fuzzy–Rough Sets for Information Measures andSelection of Relevant Genes From Microarray Data”, IEEE, Vol. 40, No. 3, P.741-752, 2010.

[10] Nho K, Shen L, Kim S, Risacher S.L, West J.D, Foroud T, Jack C.R, Weiner M.W, Saykin A.J,“Automatic Prediction of Conversion from Mild Cognitive Impairment to Probable Alzheimer's Disease using Structural Magnetic Resonance Imaging”,AMIA Annu Symp,Vol.64,pp. 542-548,2010

[11] J.Ramírez,J.M.Gorriz,F.Segovia,R.Chaves,R.Chaves,D.Salas-Gonzalez,M.Lopez,L.Alvarez and P.Padilla,“Early Alzheimer’s disease diagnosis using partial least squares and random forest”,, Neuroscience,Vol. 472, pp. 99-103,2010.

[12] Serkawt Khola, “Feature Weighting and Selection A Novel Genetic Evolutionary Approach”, World Academy of Science, Engineering and Technology 73, P.1007-1012, 2011. Ping Yao, “Fuzzy Rough Set and Information Entropy Based Feature Selection for Credit Scoring”, IEEE, P.247-251, 2009.

[13] El-sayed a. El-dahshan, Abdel-badeeh and Tamer H. Younis, “A Hybrid Technique for Automatic MRI brain Images Classification”, Studia Univ. Babes_Bolyai, Informatica, Vol. LIV, No.1, P.55-66,2009

[14] Cai W, Kim J, Feng D,“Content-based medical image retrieval”, Biomedical information technology, Elsevier,Vol. 66, pp 83–113,2008.

[15] Muller H, Michoux N, Bandon D, Geissbuhler A,“ A review of content-based image retrieval systems in medical applications – clinical benefits and future directions”, IJRRC,pp 1–23,2004.

[16] T. Rohlfing, D. B. Russakoff, R. Brandt, R. Menzel, and C. R. Maurer, “Performance-Based Multi-Classifier Decision Fusion for ATLAS-Based Segmentation of Biomedical Images”, IEEE International Symposium, 404- 407 Vol. 1,2004.

[17] Free borough P.A., Fox N.C., “MR image texture analysis applied to the diagnosis and tracking of Alzheimer’s disease ”, IEEE Transactions on Medical Imaging, Vol. 17, No.3, P. 475-479, 1998.

[18] Elisseeff A, Paugam-Moisy H,“ Size of multilayer networks for exact learning: analytic approach”, Advances in Neural Information Processing Systems, vol. 9. pp. 162–168,1997.

[19] Stehman S.V,“Selecting and interpreting measures of thematic classification accuracy”, Remote Sens Environ,pp. 77–89,1997.

[20] Chen MS, Han J, Yu PS,“ An overview from a database perspective of data mining ”,. IEEE Trans Knowledge Data Engg, Vol. 1, pp. 866-883,1996.