Comparing automated and non‐automated machine learning for autism spectrum disorders classification using facial images

Elshoky, Basma Ramadan Gamal; Younis, Eman M. G.; Ali, Abdelmgeid A.; Ibrahim, Osman Ali Sadek

doi:10.4218/etrij.2021-0097

Cited by 30 publications

(16 citation statements)

References 26 publications

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“…For future research, the integration of more diverse data sources for classification could be explored–for instance, by amalgamating various brain templates or utilizing time-frequency analysis methods to obtain additional FC information. Facial information of subjects can also be integrated for autism diagnosis [ 11 ]. Another way is merging the text information obtained from communicating with patients and performing natural language processing to assist in screening.…”

Section: Discussionmentioning

confidence: 99%

A hybrid CNN-SVM model for enhanced autism diagnosis

Qiu,

Zhai

2024

PLoS ONE

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Autism is a representative disorder of pervasive developmental disorder. It exerts influence upon an individual’s behavior and performance, potentially co-occurring with other mental illnesses. Consequently, an effective diagnostic approach proves to be invaluable in both therapeutic interventions and the timely provision of medical support. Currently, most scholars’ research primarily relies on neuroimaging techniques for auxiliary diagnosis and does not take into account the distinctive features of autism’s social impediments. In order to address this deficiency, this paper introduces a novel convolutional neural network-support vector machine model that integrates resting state functional magnetic resonance imaging data with the social responsiveness scale metrics for the diagnostic assessment of autism. We selected 821 subjects containing the social responsiveness scale measure from the publicly available Autism Brain Imaging Data Exchange dataset, including 379 subjects with autism spectrum disorder and 442 typical controls. After preprocessing of fMRI data, we compute the static and dynamic functional connectivity for each subject. Subsequently, convolutional neural networks and attention mechanisms are utilized to extracts their respective features. The extracted features, combined with the social responsiveness scale features, are then employed as novel inputs for the support vector machine to categorize autistic patients and typical controls. The proposed model identifies salient features within the static and dynamic functional connectivity, offering a possible biological foundation for clinical diagnosis. By incorporating the behavioral assessments, the model achieves a remarkable classification accuracy of 94.30%, providing a more reliable support for auxiliary diagnosis.

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Section: Discussionmentioning

confidence: 99%

A hybrid CNN-SVM model for enhanced autism diagnosis

Qiu,

Zhai

2024

PLoS ONE

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“…ResNet50: Residual Network is abbreviated as ResNet. It is a residual network with 50 layers [43, 44]. Sometimes the performance of DNN starts to decrease when add more layers.…”

Section: Proposed Methodology and Workflow Of The Frameworkmentioning

confidence: 99%

MLCNN‐COV: A multilabel convolutional neural network‐based framework to identify negative COVID medicine responses from the chemical three‐dimensional conformer

Das

Mazumder

2023

ETRI Journal

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To treat the novel COronaVIrus Disease (COVID), comparatively fewer medicines have been approved. Due to the global pandemic status of COVID, several medicines are being developed to treat patients. The modern COVID medicines development process has various challenges, including predicting and detecting hazardous COVID medicine responses. Moreover, correctly predicting harmful COVID medicine reactions is essential for health safety. Significant developments in computational models in medicine development can make it possible to identify adverse COVID medicine reactions. Since the beginning of the COVID pandemic, there has been significant demand for developing COVID medicines. Therefore, this paper presents the transfer‐learning methodology and a multilabel convolutional neural network for COVID (MLCNN‐COV) medicines development model to identify negative responses of COVID medicines. For analysis, a framework is proposed with five multilabel transfer‐learning models, namely, MobileNetv2, ResNet50, VGG19, DenseNet201, and Inceptionv3, and an MLCNN‐COV model is designed with an image augmentation (IA) technique and validated through experiments on the image of three‐dimensional chemical conformer of 17 number of COVID medicines. The RGB color channel is utilized to represent the feature of the image, and image features are extracted by employing the Convolution2D and MaxPooling2D layer. The findings of the current MLCNN‐COV are promising, and it can identify individual adverse reactions of medicines, with the accuracy ranging from 88.24% to 100%, which outperformed the transfer‐learning model's performance. It shows that three‐dimensional conformers adequately identify negative COVID medicine responses.

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“…[18,19]. These studies resulted in the development of machine-learning methods for the classification of Alzheimer's disease, mild cognitive impairment, right temporal epilepsy, schizophrenia, Parkinson's disease, dementia, attention deficit hyperactivity disorder, autism spectrum disorder, and major depressive disorder [20]. These statistical algorithm-based machinelearning models are well-suited to complex issues that require a combinatorial explosion of options or non-linear processes.…”

Section: Related Workmentioning

confidence: 99%

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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Comparing automated and non‐automated machine learning for autism spectrum disorders classification using facial images

Cited by 30 publications

References 26 publications

A hybrid CNN-SVM model for enhanced autism diagnosis

A hybrid CNN-SVM model for enhanced autism diagnosis

MLCNN‐COV: A multilabel convolutional neural network‐based framework to identify negative COVID medicine responses from the chemical three‐dimensional conformer

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

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