Facial Expression Image Analysis to Classify High and Low Level ASD Kids Using Attention Mechanism Embedded Deep Learning Technique

Gnanaprakasam, C.; Anand, Sundar; Kumar, Ramesh; Menaka, R.

doi:10.1007/978-981-15-9019-1_49

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…ResNets come in various depths, denoted by the number of layers. For instance, ResNet-18, ResNet-34, ResNet-50, ResNet-101, and ResNet-152 [27] are common variants with different depths. The deeper versions can capture more complex features from the data but require careful training and regularization to prevent overfitting.…”

Section: Resnetsmentioning

confidence: 99%

Cultivating Insight: Detecting Autism Spectrum Disorder through Residual Attention Network in Facial Image Analysis

Rajagopal,

Swaminathan

et al. 2023

IJRITCC

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Revolutionizing Autism Spectrum Disorder Identification through Deep Learning: Unveiling Facial Activation Patterns. In this study, our primary objective is to harness the power of deep learning algorithms for the precise identification of individuals with autism spectrum disorder (ASD) solely from facial image datasets. Our investigation centers around the utilization of face activation patterns, aiming to uncover novel insights into the distinctive facial features of ASD patients. To accomplish this, we meticulously examined facial imaging data from a global and multidisciplinary repository known as the Autism Face Imaging Data Exchange. Autism spectrum disorder is characterized by inherent social deficits and manifests in a spectrum of diverse symptomatic scenarios. Recent data from the Centers for Disease Control (CDC) underscores the significance of this disorder, indicating that approximately 1 in 54 children are impacted by ASD, according to estimations from the CDC's Autism and Developmental Disabilities Monitoring Network (ADDM). Our research delved into the intricate functional connectivity patterns that objectively distinguish ASD participants, focusing on their facial imaging data. Through this investigation, we aimed to uncover the latent facial patterns that play a pivotal role in the classification of ASD cases. Our approach introduces a novel module that enhances the discriminative potential of standard convolutional neural networks (CNNs), such as ResNet-50, thus significantly advancing the state-of-the-art. Our model achieved an impressive accuracy rate of 99% in distinguishing between ASD patients and control subjects within the dataset. Our findings illuminate the specific facial expression domains that contribute most significantly to the differentiation of ASD cases from typically developing individuals, as inferred from our deep learning methodology. To validate our approach, we conducted real-time video testing on diverse children, achieving an outstanding accuracy score of 99.90% and an F1 score of 99.67%. Through this pioneering work, we not only offer a cutting-edge approach to ASD identification but also contribute to the understanding of the underlying facial activation patterns that hold potential for transforming the diagnostic landscape of autism spectrum disorder.

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Section: Resnetsmentioning

confidence: 99%