Abstract
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder, affected by persistent deficits in communication and social interaction and by restricted and repetitive patterns of behavior, interests or activities. Its diagnosis is still a challenge due to the diversity between the manifestations of autistic symptoms, requiring interdisciplinary assessments. This work aims to investigate the performance of the application of techniques of extraction of characteristics and machine learning in magnetic resonance imaging (MRI), in the classification of individuals with ASD. In MRI, the techniques of features extraction were applied: histogram, histogram of oriented gradient and local binary pattern. These features were used to compose the input data of the Support Vector Machine and Artificial Neural Network algorithms. The best result shows an accuracy percentage of 89.66 and a false negative rate of 6.89%. The results obtained suggest that magnetic resonance analysis can contribute to the diagnosis of ASD from the advances in studies in the area.
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Notes
- 1.
Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
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Acknowledgements
Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research and Development, LLC; Johnson and Johnson Pharmaceutical Research and Development LLC; Lumosity; Lundbeck; Merck and Co., Inc.; Meso Scale Diagnostics, LLC; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
The authors thank to the IF Sudeste MG—Campus Juiz de Fora, through the DPIPG for the support, and all people who collaborated directly or indirectly for the construction of this work.
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Carvalho, V.F., Valadão, G.F., Faceroli, S.T., Amaral, F.S., Rodrigues, M. (2022). Performance Evaluation of Machine Learning Techniques Applied to Magnetic Resonance Imaging of Individuals with Autism Spectrum Disorder. In: Bastos-Filho, T.F., de Oliveira Caldeira, E.M., Frizera-Neto, A. (eds) XXVII Brazilian Congress on Biomedical Engineering. CBEB 2020. IFMBE Proceedings, vol 83. Springer, Cham. https://doi.org/10.1007/978-3-030-70601-2_252
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