Eye tracking-based diagnosis and early detection of autism spectrum disorder using machine learning and deep learning techniques

Ahmed, Ibrahim Abdulrab and Senan, Ebrahim Mohammed and Rassem, Taha H. and Hashem Ali, Mohammed Abdo and Shatnawi, Hamzeh Salameh Ahmad and Alwazer, Salwa Mutahar and Alshahrani, Mohammed (2022) Eye tracking-based diagnosis and early detection of autism spectrum disorder using machine learning and deep learning techniques. Electronics, 11 (4). ISSN 2079-9292, DOI https://doi.org/10.3390/electronics11040530.

Full text not available from this repository.


Eye tracking is a useful technique for detecting autism spectrum disorder (ASD). One of the most important aspects of good learning is the ability to have atypical visual attention. The eye-tracking technique provides useful information about children's visual behaviour for early and accurate diagnosis. It works by scanning the paths of the eyes to extract a sequence of eye projection points on the image to analyse the behaviour of children with autism. In this study, three artificial-intelligence techniques were developed, namely, machine learning, deep learning, and a hybrid technique between them, for early diagnosis of autism. The first technique, neural networks feedforward neural networks (FFNNs) and artificial neural networks (ANNs)], is based on feature classification extracted by a hybrid method between local binary pattern (LBP) and grey level co-occurrence matrix (GLCM) algorithms. This technique achieved a high accuracy of 99.8% for FFNNs and ANNs. The second technique used a pre-trained convolutional neural network (CNN) model, such as GoogleNet and ResNet-18, on the basis of deep feature map extraction. The GoogleNet and ResNet-18 models achieved high performances of 93.6% and 97.6%, respectively. The third technique used the hybrid method between deep learning (GoogleNet and ResNet-18) and machine learning (SVM), called GoogleNet + SVM and ResNet-18 + SVM. This technique depends on two blocks. The first block used CNN to extract deep feature maps, whilst the second block used SVM to classify the features extracted from the first block. This technique proved its high diagnostic ability, achieving accuracies of 95.5% and 94.5% for GoogleNet + SVM and ResNet-18 + SVM, respectively.

Item Type: Article
Uncontrolled Keywords: Autism spectrum disorder; Eye tracking; Machine learning; Neural networks; Convolutional neural network; GLCM; Local binary pattern
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QC Physics
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering > Department of Mechanical Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 08 Aug 2022 01:12
Last Modified: 08 Aug 2022 01:12
URI: http://eprints.um.edu.my/id/eprint/33377

Actions (login required)

View Item View Item