Effectiveness of Histogram Equalization and Ensemble Deep Learning Techniques for Detecting Autism Using Eye-Tracking (original) (raw)

Abstract

Autism spectrum disorder (ASD) is a common neurological condition marked by difficulties in social communication and the presence of repetitive behaviors. Early and accurate diagnosis is vital but remains a significant clinical challenge in ASD. This study investigates an artificial intelligence approach to differentiate between autistic and typically developing children. Deep learning models were trained on publicly available eye-tracking data from 59 participants (29 ASD, 30 TD), focusing on eye movement patterns as a potential diagnostic tool. The methodology involved comprehensive image preprocessing using histogram equalization to enhance visual feature representation, while data augmentation techniques were used to address common dataset limitations in ASD research. Transfer learning with custom layers was further employed to optimize model performance. Using state-of-the-art architectures including DenseNet169, DenseNet201, VGG16, VGG19, ResNet50, ResNet50V2, ResNet152V2, MobileNet, MobileNetV2, InceptionV3, and NASNetMobile, various classification accuracies were achieved: DenseNet169 96%, DenseNet201 96%, VGG16 96%, VGG19 95%, ResNet50 93%, ResNet50V2 92%, ResNet152V2 94%, MobileNet 96%, MobileNetV2 94%, InceptionV3 85%, and NASNetMobile 91%, with corresponding sensitivities ranging from 82 to 97% and specificities from 87 to 97%. An ensemble model combining optimized VGG16, MobileNet, DenseNet169, and Vision Transformer ViT architectures achieved a classification accuracy of 98% with 98% sensitivity and 97% specificity. The results demonstrate the potential of combining advanced deep learning techniques with eye-tracking technology for developing more accurate and objective ASD diagnostic tools.

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Data Availability

Abbreviations

ASD:

Autism spectrum disorder

TD:

Typically developing

CNN:

Convolutional neural network

ROC:

Receiver operating characteristic

AUC:

Area under the curve

VGG:

Visual geometry group

ViT:

Vision transformer

LSTM:

Long short-term memory

SVM:

Support vector machine

RF:

Random forest

DT:

Decision tree

KNN:

K-nearest neighbors

MLP:

Multilayer perceptron

LR:

Logistic regression

NB:

Naive bayes

CNN-LSTM:

Convolutional neural networks and long short-term memory networks

PCA:

Principal component analysis

RGB:

Red, green, blue

ReLU:

Rectified linear unit

FC:

Fully connected

BN:

Batch normalization

Conv:

Convolution

AI:

Artificial intelligence

EEG:

Electroencephalography

MRI:

Magnetic resonance imaging

CDC:

Centers for disease control and prevention

ADDM:

Autism and developmental disabilities monitoring

ROI:

Region of interest

TPR:

True positive rate

FPR:

False positive rate

TP:

True positive

TN:

True negative

FP:

False positive

FN:

False negative

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Funding

This work was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [Grant: KFU260672].

Author information

Authors and Affiliations

  1. Faculty of Data Science and Information Technology, INTI International University, Nilai, 71800, Negeri Sembilan, Malaysia
    Zeyad A. T. Ahmed
  2. Applied College, King Faisal University, P.O. Box 400, Al-Ahsa, 31982, Saudi Arabia
    Theyazn H. H. Aldhyani
  3. Deanship of E-Learning and Distance Education and Information Technology, King Faisal University, P.O. Box 4000, Al-Ahsa, 31982, Saudi Arabia
    Mosleh Hmoud Al-Adhaileh & Ahmed Samir Morsy
  4. Computer Science Department, Al-Baha University, Al-Baha, 65779, Saudi Arabia
    Eidah M. Alzahrani & Mohammad H. Algarni
  5. School of Computer Science and Information Technology, King Faisal University, P.O. Box 4000, Al-Ahsa 31982,,, Saudi Arabia
    Eid Albalawi & Ali Mehdi
  6. Shri Shivaji Science and Arts College, Chikhli Dist., Buldana, India
    Mukti E. Jadhav
  7. Department of Technology, College of Technology and Business, Riyadh Elm University, Riyadh, Saudi Arabia
    Saleh N. M. Alsubari

Authors

  1. Zeyad A. T. Ahmed
  2. Theyazn H. H. Aldhyani
  3. Mosleh Hmoud Al-Adhaileh
  4. Eidah M. Alzahrani
  5. Eid Albalawi
  6. Mohammad H. Algarni
  7. Mukti E. Jadhav
  8. Saleh N. M. Alsubari
  9. Ahmed Samir Morsy
  10. Ali Mehdi

Contributions

All authors contributed significantly to the completion of this article, but they had different roles in all aspects. Zeyad A.T.Ahmed 1, Theyazn H.H Aldhyani2, Eidah M Alzahrani3 Conceptualization, Zeyad A.T.Ahmed 1, Theyazn H.H Aldhyani2, Eidah M Alzahrani3 Data curation, Eid Albalawi4, Mohammad H Algarni5, Mukti E. Jadhav6 Methodology: Zeyad A.T.Ahmed 1, Theyazn H.H Aldhyani2, Eidah M Alzahrani3, Sultan Ahmad7,8, Mosleh Hmoud Al-Adhaileh9, Saleh Nagi Alsubari1 and Ali Mehdi4 Writing—review & editing, Eid Albalawi4, Mohammad H Algarni5, Mukti E. Jadhav6, Sultan Ahmad7,8, Mosleh Hmoud Al-Adhaileh9 ,Saleh Nagi Alsubari1 and Ali Mehdi4 All authors have read and agreed to the published version of the manuscript.

Corresponding authors

Correspondence toZeyad A. T. Ahmed or Theyazn H. H. Aldhyani.

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Appendix

Appendix

See Table 8.

Table 8 Standard deviation results of deep learning classification results before the preprocessing and enhancement

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Ahmed, Z.A.T., Aldhyani, T.H.H., Al-Adhaileh, M.H. et al. Effectiveness of Histogram Equalization and Ensemble Deep Learning Techniques for Detecting Autism Using Eye-Tracking.SN COMPUT. SCI. 7, 245 (2026). https://doi.org/10.1007/s42979-026-04825-9

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