Autism spectrum disorder identification using multi‐model deep ensemble classifier with transfer learning

Author:

Herath Lakmini1,Meedeniya Dulani2ORCID,Marasinghe Janaka3,Weerasinghe Vajira4,Tan Tele5

Affiliation:

1. Postgraduate Institute of Science University of Peradeniya Peradeniya Sri Lanka

2. Department of Computer Science and Engineering University of Moratuwa Moratuwa Sri Lanka

3. Department of Radiography/Radiotherapy, Faculty of Allied Health Science University of Peradeniya Peradeniya Sri Lanka

4. Department of Physiology, Faculty of Medicine University of Peradeniya Peradeniya Sri Lanka

5. School of Electrical Engineering, Computing, and Mathematical Sciences Curtin University Perth Western Australia Australia

Abstract

AbstractIdentifying autism spectrum disorder (ASD) symptoms accurately is a challenging task. The traditional subjective diagnostic process of ASD relies on time‐consuming behavioural and psychological observations. In this study, we introduce an ensemble learning‐based classification model using an open‐access database focusing on functional magnetic resonance imaging (fMRI). We propose a novel multi‐model ensemble classifier (MMEC) and multisite ensemble classifier (MSEC) with transfer learning (TL) for ASD classification to improve the prediction accuracy. The MMEC utilizes four base classifiers, Inception V3, ResNet50, MobileNet, and DenseNet to boost the performance of the individual convolutional neural network (CNN) models. The MSEC combined the base classifiers trained from different data sites. We evaluate the two models with ensemble averaging, weighted averaging, and stacking methods. The proposed MMEC with stacking shows the state of art performance compared to MSEC, improving the prediction accuracy by 3.25%. The obtained results have shown an accuracy of 97.82%, 97.82%, and 97.78% for ensemble averaging, weighted averaging, and stacking methods, respectively, on multi‐site datasets. The ensemble classifier MMEC performed better than a single classifier on the multi‐site dataset. The proposed MMEC opens a new paradigm to design a universal ASD classification framework.

Publisher

Wiley

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