Abstract
Autism spectrum disorder also known as ASD is a convoluted neuro developmental condition particularly related to nervous system that affects people’s communication, social behavior, and underlying social knowledge. Detection of ASD always remains challenging due to its complicated psychiatric symptoms. Traditional clinical tools used for the detection of ASD are not much efficient in earlier ASD diagnosis. To overcome the limitations of existing methodologies, biomarkers were used in the detection of neurological dysfunctions. Magnetic resonance imaging has proved to be an efficient biomarker in ASD diagnosis which delineates the anatomical and morphological regions of the brain. Accuracy rate can be improved further by using multi-modal features, e.g., combination of structural MRI and functional MRI. This paper presents ASD_sfMRI framework which uses combination of sMRI and fMRI data for providing comprehensive information about ASD diagnosis. ASD_sfMRI framework will work in four phases: (i) Structural MRI analysis (ii) Functional MRI analysis (iii) Fusion of structural MRI and functional MRI parameters, and finally, (iv) deep learning-based automated prediction model is used for final diagnosis.
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Jamwal, I., Malhotra, D., Mengi, M. (2022). Autism Spectrum Disorder Detection Using ASD_sfMRI. In: Bansal, J.C., Engelbrecht, A., Shukla, P.K. (eds) Computer Vision and Robotics. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-8225-4_14
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DOI: https://doi.org/10.1007/978-981-16-8225-4_14
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