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Comparative Study on ASD Identification Using Machine and Deep Learning
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Author(s): Rajandeep Kaur (Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, India)and Rajneesh Rani (National Institute of Technology, Jalandhar, India)
Copyright: 2020
Pages: 21
Source title:
Interdisciplinary Approaches to Altering Neurodevelopmental Disorders
Source Author(s)/Editor(s): Tanu Wadhera (Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, India)and Deepti Kakkar (Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, India)
DOI: 10.4018/978-1-7998-3069-6.ch015
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Abstract
Autism spectrum disorder (ASD) is a neurodevelopment disorder that consists of a lack of social interaction and repetitive behavior of a person. It must be diagnosed at an early stage; otherwise it may adversely affect the life of that person. However, ASD consists of numerous overlapping neurodevelopment disorders such as autism and schizophrenia, which share some common brain structures. The medical image analysis is very popular to identify and segment different diseases in healthcare. In recent years, due to the tremendous changes in imaging devices, neuroimaging has recorded a great increase in identifying neuropsychiatric disorders. Moreover, MRI (magnetic resonance imaging) is a powerful non-invasive medical imaging to distinguish between these overlapping diseases by extracting brain connectivity. The researchers have applied numerous algorithms to understand common and distinctive brain connectivity features for these disorders. This chapter will focus on state-of-the-art techniques based on machine and deep learning with their comparative analysis and challenges of ASD.
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