Iranian Journal of Biomedical Engineering (IJBME)
نشریه علمی مهندسی پزشکی زیستی

IQ Estimation from MRI Images using a Combination of Convolutional Neural Network and XGBoost Algorithm

Document Type : Full Research Paper

Authors

Shayan Rostami 1
Hossein Sadr 2
Seyed Ahmad Edalatpanah 3
Mojdeh Nazari 4

1 M.Sc., Department of Computer Engineering, Ayandegan Institute of Higher Education, Tonekabon, Iran

2 Assistant Professor, Department of Health Informatics, Guilan Road Trauma Research Center, Trauma Institute, Guilan University of Medical Sciences, Rasht, Iran

3 Associate Professor, Department of Computer Engineering, Ayandegan Institute of Higher Education, Tonekabon, Iran

4 Assistant Professor, Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

https://doi.org/10.22041/ijbme.2024.2031836.1906
Abstract
IQ test is a common solution to detect and measure people's intelligence, but studies show that the brain activity of intelligent people is significantly different compared to people with normal and low IQ, and this difference can be detected from advanced magnetic resonance imaging or MRI. In this regard, the aim of this paper is to present a model based on artificial intelligence to estimate people's IQ from brain MRI images. To achieve this goal, the combination of Convolutional Neural Network (CNN) and eXtreme Gradient Boosting (XGBoost) is used in this paper. Convolutional neural networks excel in extracting features from images and make them effective for processing visual stimuli related to IQ tests. On the other hand, the XGBoost algorithm is a powerful ensemble algorithm that can effectively combine the predictions of multiple models to improve overall accuracy. In the proposed model, ResNet-50 and VGG16 were used as a feature extractor and the XGBoost algorithm was used as an identifier at the top level of the network to generate results. The proposed model was tested on the Autism Brain Imaging Information Exchange (ABIDE) brain image dataset. Based on the results of the experiments, the use of the pre-trained convolutional neural network VGG16 showed a better performance than the pre-trained convolutional neural network ResNet-50 in the feature extraction section, and its combination with XGBoost algorithm obtained an accuracy of 83% achieved against 60% accuracy of ResNet-50.

Keywords

IQ
Deep Learning
Convolutional Neural Network
XGBoost Algorithm
MRI Images

Subjects

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Iranian Journal of Biomedical Engineering (IJBME)
Volume 18, Issue 1
Spring 2024
Pages 21-34

  • Receive Date 10 June 2024
  • Revise Date 13 November 2024
  • Accept Date 26 November 2024

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