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Brain age estimation based on 3D MRI images using 3D convolutional neural network

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Abstract

Brain Age Estimation (BAE) has become a popular challenge in the field of medical and computer sciences in recent years. In the medical sciences field, the investigation on the brain structure and its relationship with aging is considerable. In the computer sciences field, creating an efficient Machine Learning (ML) model of BAE would lead to have accurate regression models. In this paper, a 3D Convolutional Neural Network (3D-CNN) model is used to train a brain age estimation system. To reach a more accurate system, two other regression methods are also applied on the final feature vector generated by 3D-CNN system. The system is applied on the samples of IXI dataset normalized by SPM14. Next, to ensure the model’s generalization, 47 healthy samples of ADNI1 dataset are used. Furthermore, some MRI images achieved from Alzheimer patients are feed to the proposed model and the effects of Alzheimer disease on brain aging are investigated. The best Mean Absolute Error (MAE) on evaluation dataset is about 5 years, with Root Mean Square Error (RMSE) = 13.5. The model generalization by a new healthy dataset was evaluated and the result is with the MAE value of about 6 years.

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Notes

  1. Philadelphia Neurodevelopmental Cohort

  2. http://fantail.doc.ic.ac.uk

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Acknowledgements

This work was supported by Iranian National Science Foundation (INSF with contract No. 91060887).

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  1. School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran

    Nastaran Pardakhti & Hedieh Sajedi

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  1. Nastaran Pardakhti
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  2. Hedieh Sajedi
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Correspondence to Hedieh Sajedi.

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Pardakhti, N., Sajedi, H. Brain age estimation based on 3D MRI images using 3D convolutional neural network. Multimed Tools Appl 79, 25051–25065 (2020). https://doi.org/10.1007/s11042-020-09121-z

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  • DOI: https://doi.org/10.1007/s11042-020-09121-z

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