Diagnosis of Autism Spectrum Disorder Through Eye Movement Tracking Using Deep Learning

Mumenin, Nasirul; Islam, Md. Farhadul; Chowdhury, Md. Reasad Zaman; Yousuf, Mohammad Abu

doi:10.1007/978-981-19-7528-8_20

Cited by 9 publications

(2 citation statements)

References 15 publications

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“…The principal goals of utilizing ML techniques are to expedite access to healthcare services by increasing precision in diagnosis and reducing the diagnostic time. The diagnosis method of a particular instance can be considered a task related to classification in ML as it entails determining the appropriate class (ASD, No-ASD) according to the properties of the input case (Mumenin et al, 2023). In previous studies, researchers employed a variety of classification strategies to achieve higher accuracy in identifying ASD cases using different datasets.…”

Section: Introductionmentioning

confidence: 99%

An Auto-encoded Warm Equilibrium Automated Learning (AE 2L) Model for Automatic Recognition and Classification of Autism Spectrum Disorder

Al-Muhanna,

Alghamdi,

Alrfaei

et al. 2024

Journal of Disability Research

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Autism spectrum disorder (ASD) is a neurological condition characterized by difficulties with communication and socializing, and repetitive activities. If the underlying reason is hereditary, early detection is still important, and machine learning offers a fascinating way to identify the condition more rapidly and economically. However, the unique issues of higher computational costs, longer execution times, and lower effectiveness affect the traditional methods. The proposed project aims to create an automated artificial intelligence tool for ASD identification that combines several state-of-the-art mining techniques to deliver the best possible level of disease prediction accuracy. For accurate and effective ASD identification, this research suggests an automated and lightweight method dubbed the auto-encoded warm equilibrium automated learner. To speed up the handicap detection process, a unique warm optimized feature selection methodology is applied to minimize the dimensionality of attributes. In addition, auto-encoded term memory equilibrium learning, a powerful deep learning technique, is designed to accurately and less frequently detect ASD from the given data. Moreover, the classifier performs better when hyperparameters are tuned using the equilibrium optimization model. The results of the proposed AE2L model have been tested and validated using a variety of parameters utilizing the well-known ASD dataset that was taken from the UCI repository.

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Section: Introductionmentioning

confidence: 99%

An Auto-encoded Warm Equilibrium Automated Learning (AE 2L) Model for Automatic Recognition and Classification of Autism Spectrum Disorder

Al-Muhanna,

Alghamdi,

Alrfaei

et al. 2024

Journal of Disability Research

View full text Add to dashboard Cite

show abstract

“…Functionally interconnected region of the brain was detected with the correlation of different region. In recent years, the deep learning based approaches are proposed due to automatic feature learning and handling complex data [18], [19], [20]. Deep learning techniques such as Multi-Layer Perceptron (MLP) with unsupervised training of stacked auto encoders are used for high classification accuracy [21], [22].…”

Section: Introductionmentioning

confidence: 99%

Autism Detection of MRI Brain Images Using Hybrid Deep CNN With DM-Resnet Classifier

Jain,

Tripathy,

Mallik

et al. 2023

IEEE Access

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The neurodevelopmental Autism Spectrum Disorder (ASD) causes problems in social communication. Earlier diagnosis of ASD from brain image is necessary for reducing the effect of disorder. In this paper, deep Convolutional Neural Network (CNN) with Dwarf Mongoose optimized Residual Network (DM-ResNet) is proposed for the classification of autism disorder from Magnetic Resonance Imaging (MRI) brain images. Initially, the input brain images are preprocessed to remove the non-brain tissues. The preprocessed images are segmented with hybrid Fuzzy C Means (FCM) and Gaussian Mixture Model (GMM) which partition the image into sub groups to make it easier for classification by reducing the complexity. FCM-GMM segments the volume into predefined cortical and sub cortical regions. After segmentation, the features are extracted with Visual Geometry Group (VGG)-16 networks which comprised of several tiny kernels with filters for enhancing the depth of network and permit to extract complicated and discriminative features. Region of Interest (ROI) based functional connectivity feature is extracted with VGG-16 and these features are classified with DM optimized ResNet. The hyper parameters are optimized with DM optimization algorithm which improves the accuracy of classifier. By using the proposed approach, the accuracy of autism detection is improved to 99.83%.

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ASDNet: A robust involution‐based architecture for diagnosis of autism spectrum disorder utilising eye‐tracking technology

Mumenin,

Yousuf,

Nashiry

et al. 2024

IET Computer Vision

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Autism Spectrum Disorder (ASD) is a chronic condition characterised by impairments in social interaction and communication. Early detection of ASD is desired, and there exists a demand for the development of diagnostic aids to facilitate this. A lightweight Involutional Neural Network (INN) architecture has been developed to diagnose ASD. The model follows a simpler architectural design and has less number of parameters than the state‐of‐the‐art (SOTA) image classification models, requiring lower computational resources. The proposed model is trained to detect ASD from eye‐tracking scanpath (SP), heatmap (HM), and fixation map (FM) images. Monte Carlo Dropout has been applied to the model to perform an uncertainty analysis and ensure the effectiveness of the output provided by the proposed INN model. The model has been trained and evaluated using two publicly accessible datasets. From the experiment, it is seen that the model has achieved 98.12% accuracy, 96.83% accuracy, and 97.61% accuracy on SP, FM, and HM, respectively, which outperforms the current SOTA image classification models and other existing works conducted on this topic.

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Diagnosis of Autism Spectrum Disorder Through Eye Movement Tracking Using Deep Learning

Cited by 9 publications

References 15 publications

An Auto-encoded Warm Equilibrium Automated Learning (AE 2L) Model for Automatic Recognition and Classification of Autism Spectrum Disorder

An Auto-encoded Warm Equilibrium Automated Learning (AE 2L) Model for Automatic Recognition and Classification of Autism Spectrum Disorder

Autism Detection of MRI Brain Images Using Hybrid Deep CNN With DM-Resnet Classifier

ASDNet: A robust involution‐based architecture for diagnosis of autism spectrum disorder utilising eye‐tracking technology

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