Autism Screening Tools With Machine Learning and Deep Learning Methods: A Review

Pavithra, D.; Jayanthi, A. N.; Nidhya, R.; Balamurugan, S.

doi:10.1002/9781119841937.ch10

Cited by 3 publications

(2 citation statements)

References 48 publications

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“…Streamlit is one of the recent and fastest python-based model deployment tools. This open-sourced python based framework simplified the whole model deployment cycle along with providing an easy way to structure the functionalities of the interface [45,46]. Once the neural network was trained, the model of an epoch with the best validation accuracy was saved as a ".model" file.…”

Section: E Uploading the Video To The Web Interfacementioning

confidence: 99%

“…The interface enables the users to select the type of media file that is to be uploaded. If the selected media type is an image, the saved model is used to classify the uploaded image to Autistic/Non-Autistic for which the result can be displayed on the interface [45]. But if the selected media type is a video, then the uploaded video is divided into frames using OpenCV and each image is classified into Autistic/Nonautistic with the help of the saved model.…”

Section: E Uploading the Video To The Web Interfacementioning

confidence: 99%

See 1 more Smart Citation

Autism Spectrum Disorder Detection: Video Games based Facial Expression Diagnosis using Deep Learning

Derbali¹,

Jarrah²,

Randhawa³

2023

IJACSA

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In this study, a novel method is proposed for determining whether a child between the ages of 3 and 10 has autism spectrum disorder. Video games have the ability to immerse a child in an intense and immersive environment. With the expansion of the gaming industry over the past decade, the availability and customization of games for children has increased dramatically. When children play video games, they may display a variety of facial expressions and emotions. These facial expressions can aid in the diagnosis of autism. Footage of children playing a game may yield a wealth of information regarding behavioral patterns, especially autistic behavior. You can submit any video of a child playing a game to the interface, which is powered by the algorithm presented in this work. We utilized a dataset of 2,536 facial images of autistic and typically developing children for this purpose. The accuracy and loss function are presented to examine the 92.3% accurate prediction outcomes generated by the CNN model and deep learning.

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Section: E Uploading the Video To The Web Interfacementioning

confidence: 99%

Section: E Uploading the Video To The Web Interfacementioning

confidence: 99%

Autism Spectrum Disorder Detection: Video Games based Facial Expression Diagnosis using Deep Learning

Derbali¹,

Jarrah²,

Randhawa³

2023

IJACSA

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Attention-Focused Eye Gaze Analysis to Predict Autistic Traits Using Transfer Learning

Vasant Bidwe,

Mishra,

Kamini Bajaj

et al. 2024

Int J Comput Intell Syst

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Autism spectrum disorder (ASD) is a complex developmental issue that affects the behavior and communication abilities of children. It is extremely needed to perceive it at an early age. The research article focuses on attentiveness by considering eye positioning as a key feature and its implementation is completed in two phases. In the first phase, various transfer learning algorithms are implemented and evaluated to predict ASD traits on available open-source image datasets Kaggle and Zenodo. To reinforce the result, fivefold cross-validation is used on the dataset. Progressive pre-trained algorithms named VGG 16, VGG 19, InceptionV3, ResNet152V2, DenseNet201, ConNextBase, EfficientNetB1, NasNetMobile, and InceptionResNEtV2 implemented to establish the correctness of the result. The result is being compiled and analyzed that ConvNextBase model has the best diagnosing ability on both datasets. This model achieved a prediction accuracy of 80.4% on Kaggle with a batch size of 16, a learning rate of 0.00002, 10 epochs and 6 units, and a prediction accuracy of 80.71% on the Zenodo dataset with a batch size of 4, a learning rate of 0.00002, 10 epochs and 4 units. The accuracy of the model ConvNextBase is found challenging in nature as compared to an existing model. Attentiveness is a parameter that will accurately diagnose the visual behavior of the participant which helps in the automatic prediction of autistic traits. In the second phase of the proposed model, attentiveness is engrossed in identifying autistic traits. The model uses a dlib library that uses HOG and Linear SVM-based face detectors to identify a particular facial parameter called EAR and it is used to measure participants' attentiveness based on the eye gaze analysis. If the EAR value is less than 0.20 for more than 100 consecutive frames, the model concludes the participant is un-attentive. The model generated a special graph for a time period by continuously plotting the value of EAR based on the attention level. The average EAR value will depict the attentiveness of the participant.

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Investigating multiclass autism spectrum disorder classification using machine learning techniques

Bawa,

Kadyan,

Mantri

et al. 2024

e-Prime - Advances in Electrical Engineering, Electronics and E

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Autism Screening Tools With Machine Learning and Deep Learning Methods: A Review

Cited by 3 publications

References 48 publications

Autism Spectrum Disorder Detection: Video Games based Facial Expression Diagnosis using Deep Learning

Autism Spectrum Disorder Detection: Video Games based Facial Expression Diagnosis using Deep Learning

Attention-Focused Eye Gaze Analysis to Predict Autistic Traits Using Transfer Learning

Investigating multiclass autism spectrum disorder classification using machine learning techniques

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