A granular functional network classifier for brain diseases analysis

References

• Bede B, Rudas IJ. 2011. Approximation properties of fuzzy transforms. Fuzzy Sets Syst. 180:20–40.
   Web of Science ®Google Scholar
• Blumberg SJ, Bramlett MD, Kogan MD, Schieve LA, Jones JR, Lu MC. 2013. Changes in prevalence of parent–reported autism spectrum disorder in school–aged US children: 2007 to 2011–2012, n. 65, US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics.
   Google Scholar
• Brown MR, Sidhu GS, Greiner R, Asgarian N, Bastani M, Silverstone PH, Greenshaw AJ, Dursun SM. 2012. ADHD-200 Global Competition: diagnosing ADHD using personal characteristic data can outperform resting state fMRI measurements. Frontiers Syst Neur. 6:69–77.
   PubMedGoogle Scholar
• Castillo E. 1998. Functional networks. Neural Proces Lett. 7:151–159.
   Web of Science ®Google Scholar
• Das R. 2010. A comparison of multiple classification methods for diagnosis of Parkinson disease. Expert Syst Appl. 37:1568–1572.
   Web of Science ®Google Scholar
• Deshpande G, Wang P, Rangaprakash D, Wilamowski B. 2015. Fully connected cascade artificial neural network architecture for attention deficit hyperactivity disorder classification from functional magnetic resonance imaging data. IEEE Trans Cybern. 45(12):2668–2679.
   PubMed Web of Science ®Google Scholar
• C. Ecker, A. Marquand, J. Mourão-Miranda, P. Johnston, E.M. Daly, M.J. Brammer, S. Maltezos, CM. Murphy, D. Robertson, SC. Williams, DGM. Murphy. 2010. Describing the brain in autism in five dimensions magnetic resonance imaging–assisted diagnosis of autism spectrum disorder using a multiparameter classification approach. J Neurosci. 30(32):10612–10623.
   PubMed Web of Science ®Google Scholar
• Itani S, Lecron F, Fortemps P. 2018. A multi-level classification framework for multi-site medical data: application to the ADHD-200 collection. Expert Syst Appl. 91:36–45.
   Web of Science ®Google Scholar
• Itani S, Lecron F, Fortemps P. 2019. Specifics of medical data mining for diagnosis aid: a survey. Expert Syst Appl. 118:300–314.
   Web of Science ®Google Scholar
• Jacquelin M, Lin L, Yang C. 2018. PSelInv A distributed memory parallel algorithm for selected inversion: the non-symmetric case. Parallel Comput. 74:84–98.
   Web of Science ®Google Scholar
• Jaeger H. 2002. Adaptive nonlinear system identification with echo state networks. In: Advances in neural information processing systems. Massachusetts: MIT Press Cambridge. p. 593–600.
   Google Scholar
• Katuwal GJ, Cahill ND, Baum SA, Michael AM. 2015. The predictive power of structural MRI in autism diagnosis. Proceedings of 2015 37th annual inter- national conference of the IEEE Engineering in Medicine and Biology Society (EMBC); Milan, Italy: IEEE Computer Society. p. 4270–4273.
   Google Scholar
• Kessler RC, Adler L, Barkley R, Biederman J, Conners CK, Demler O, Faraone SV, Greenhill LL, Howes MJ, Secnik K, et al. 2006. The prevalence and correlates of adult ADHD in the united states: results from the national comorbidity survey replication. Am J Psychiatry. 163(4):716–723.
   PubMed Web of Science ®Google Scholar
• Kong Y, Gao J, Xu Y, Pan Y, Wang J, Liu J. 2019. Classification of autism spectrum disorder by combining brain connectivity and deep neural network classifier. Neurocomputing. 324:63–68.
   Web of Science ®Google Scholar
• Leite D, Costa P, Gomide F. 2013. Evolving granular neural networks from fuzzy data streams. Neural Netw. 38:1–16.
   PubMed Web of Science ®Google Scholar
• Lipton ZC. 2016. The mythos of model interpretability. arXiv Preprint. arXiv:1606.03490.
   Google Scholar
• Loia V, Parente D, Pedrycz W, Tomasiello S. 2018. A granular functional network with delay: some dynamical properties and application to the sign prediction in social networks. Neurocomp. 321:61–71.
   Web of Science ®Google Scholar
• Loia V, Tomasiello S. 2017. Granularity into functional networks. Proceedings of the Third IEEE International Conference on Cybernetics (CYBCONF 2017); Exeter, UK: IEEE Computer Society. p. 1–6.
   Google Scholar
• Lu W, Pedrycz W, Liu X, Yang J, Li P. 2014. The modeling of time series based on fuzzy information granules. Expert Syst Appl. 41:3799–3808.
   Web of Science ®Google Scholar
• Mencar C, Castellano G, Fanelli AM. 2007. On the role of interpretability in fuzzy data mining. Int J Uncert Fuzz Knowl Syst. 15(05):521–537.
   Google Scholar
• Patra JC, Pal RN, Chatterji BN, Panda G. 1999. Identification of nonlinear dynamic systems using functional link artificial neural networks. IEEE Trans Syst Man Cybern B. 29(2):254–262.
   PubMed Web of Science ®Google Scholar
• Pedrycz W, Vukovich W. 2001a. Granular neural networks. Neurocomp. 36:205–224.
   Web of Science ®Google Scholar
• Pedrycz, Vukovich. 2001b. Abstraction and specialization of information granules. IEEE Trans Syst Man Cybern B. 31(1):106–111.
   PubMed Web of Science ®Google Scholar
• Siuly S, Zhang Y. 2016. Medical big data: neurological diseases diagnosis through medical data analysis. Data Sci Eng. 1(2):54–64.
   Google Scholar
• Tveit A. 2003. On the complexity of matrix inversion. Math Note. 1 (Techical Report). Norwegian University of Science and Technology, Trondheim, Norway.
   Web of Science ®Google Scholar
• Wagholikar KB, Sundararajan V, Deshpande AW. 2012. Modeling paradigms for medical diagnostic decision support: a survey and future directions. J Medical Syst. 36(5):3029–3049.
   PubMed Web of Science ®Google Scholar
• Willcutt EG. 2012. The prevalence of DSM-IV attention-deficit/hyperactivity disorder: a meta-analytic review. Neurotherapeutics. 9(3):490–499.
   PubMed Web of Science ®Google Scholar
• Wu. C, Luo. N, Xiong W. Zhang, TH, Kim. 2018. A greedy deep learning method for medical disease analysis (IEEE access). 6:20021–20030.
   Google Scholar
• Xiao X, Fang H, Wu J, Xiao C, Xiao T, Qian L, Liang F, Xiao Z, Chu KK, Ke X. 2017. Diagnostic model generated by MRI-derived brain features in toddlers with autism spectrum disorder. Autism Res. 10(4):620–630.
   PubMed Web of Science ®Google Scholar
• Xiong NX, Vasilakos A, Yang L, Song L, Pan Y, Kannan R, Li Y. 2009. Comparative analysis of quality of service and memory usage for adaptive failure detectors in healthcare systems. IEEE J Sel Areas Commun. 27(4):495–509.
   Web of Science ®Google Scholar
• Yu Y-S, Gu D-H. 1997. A note on a lower bound for the smallest singular value. Linear Algebra Appl. 252:25–38.
   Google Scholar
• Zhou L, Zheng J, Miao C, Mckeown MJ, Wang ZJ. 2017. 3D CNN based automatic diagnosis of attention deficit hyperactivity disorder using functional and structural MRI. IEEE Access. 5:23626–23656.
   Web of Science ®Google Scholar