Developing a multi-modal dataset for deep learning-based neural networks in autism spectrum disorder diagnosis
This study introduces a novel multi-modal dataset that has been meticulously curated to significantly improve diagnostic capabilities for Autism Spectrum Disorder (ASD). The dataset is intended to exploit the inherent diagnostic potential of facial images by emphasizing their systematic analysis....
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| Main Authors: | , , , |
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| Format: | Proceeding Paper |
| Language: | English English |
| Published: |
AIP publishing
2024
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/115388/7/115388_Developing%20a%20multi-modal%20dataset.pdf http://irep.iium.edu.my/115388/8/115388_Developing%20a%20multi-modal%20dataset_Scopus.pdf http://irep.iium.edu.my/115388/ https://pubs.aip.org/aip/acp/article-abstract/3161/1/020123/3310612/Developing-a-multi-modal-dataset-for-deep-learning?redirectedFrom=fulltext https://doi.org/10.1063/5.0229867 |
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| Institution: | Universiti Islam Antarabangsa Malaysia |
| Language: | English English |
| Summary: | This study introduces a novel multi-modal dataset that has been meticulously curated to significantly improve
diagnostic capabilities for Autism Spectrum Disorder (ASD). The dataset is intended to exploit the inherent diagnostic
potential of facial images by emphasizing their systematic analysis. It is comprised of video excerpts from the Self-
Stimulatory Behavior Dataset (SSBD) demonstrating behaviors such as hand flapping, head banging, and spinning, as well
as 50 videos from ASD therapists and specialized educational institutions. In order to establish a comparative baseline, 100
videos from traditional educational contexts were collected for the Normal control group. Face Recognition (FR) pipeline
with MTCNN is used for facial recognition, followed by precision-driven cropping, alignment, and scaling stages, resulting
in RGB (2D) facial representations. Moreover, the incorporation of 3DDFA-V2 facilitates the transformation of 2D RGB
images into a comprehensive 3D dataset by means of depth maps. The dataset contains 173 individuals aged 1 to 11 years
in the Normal control group and 123 individuals, including 93 males and 30 females, in the ASD group. The data was
divided into training and testing sets, with 1068 training samples and 100 testing samples, respectively. Open access to this dataset for researchers, medical professionals, and technologists will foster collaborative efforts in the evelopment of automated ASD diagnostic methods, leveraging multi-modal data and deep learning to improve diagnostic precision and efficiency |
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