Autism spectrum self-stimulatory behaviours classification using explainable temporal coherency deep networks and SVM classifier / Liang Shuaibing
Autism spectrum disorder is a very common disorder. An early diagnosis of autism is essential for the prognosis of this disorder. The common method for diagnosis utilizes behavioural cues of autistic children. Doctors require years of clinical training to acquire the ability to capture these behavio...
Saved in:
Main Author: | Liang , Shuaibing |
---|---|
Format: | Thesis |
Published: |
2022
|
Subjects: | |
Online Access: | http://studentsrepo.um.edu.my/14424/1/Liang_Shuaibing.pdf http://studentsrepo.um.edu.my/14424/2/Liang_Shuaibing.pdf http://studentsrepo.um.edu.my/14424/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Autism spectrum self-stimulatory behaviors classification using explainable temporal coherency deep features and SVM classifier
by: Liang, Shuaibing, et al.
Published: (2021) -
Autism spectrum disorder classification in videos: A hybrid of temporal coherency deep networks and self-organizing dual memory approach
by: Liang, Shuaibing, et al.
Published: (2020) -
Glioma Tumors’ Classification Using Deep-Neural-Network-Based Features with SVM Classifier
by: Ghazanfar, Latif, et al.
Published: (2022) -
Autism Spectrum Disorder Classification Using Deep Learning
by: Abdulrazak Yahya, Saleh, et al.
Published: (2021) -
Employing explainability on facial landmarks for autism
spectrum disorder diagnosis using deep CNN
by: Alam, Mohammad Shafiul, et al.
Published: (2024)