A Review on Deep Learning Algorithms in the Detection of Autism Spectrum Disorder

被引:1
作者
Lamani, Manjunath Ramanna [1 ]
Benadit, P. Julian [1 ]
机构
[1] CHRIST, Mysor Rd, Bangalore 560074, Karnataka, India
来源
FOURTH CONGRESS ON INTELLIGENT SYSTEMS, VOL 3, CIS 2023 | 2024年 / 865卷
关键词
Deep learning; ASD; CNN; RNN; LSTM; CRNN algorithms; INDIVIDUALS;
D O I
10.1007/978-981-99-9043-6_24
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Autism spectrum disorder (ASD) is a neurodisorder that has an impact on how people interact and communicate with each other for the rest of their lives. Most autistic symptoms appear throughout the first two years of a child's life. This is why autism is called a behavioral disease. If you have a child with ASD, the problem starts in childhood and keeps going through adolescence and adulthood. Deep learning techniques are becoming more common in research on medical diagnosis. In this paper, there is an effort to see if convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory network (LSTM), and a fusion technique known as convolutional recurrent neural network (CRNN) can be used to detect ASD problems in a child, adolescents, and adults.
引用
收藏
页码:283 / 297
页数:15
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