Brain Age Prediction Using a Lightweight Convolutional Neural Network
Much interest has recently been drawn to brain age prediction due to the significant development in machine learning and image processing techniques. Studies based on brain magnetic resonance images showed a strong relationship between the brain ageing process and accelerated brain atrophy, suggesti...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10829597/ |
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| author | Fatma Eltashani Mario Parreno-Centeno James H. Cole Joao Paulo Papa Fumie Costen |
| author_facet | Fatma Eltashani Mario Parreno-Centeno James H. Cole Joao Paulo Papa Fumie Costen |
| author_sort | Fatma Eltashani |
| collection | DOAJ |
| description | Much interest has recently been drawn to brain age prediction due to the significant development in machine learning and image processing techniques. Studies based on brain magnetic resonance images showed a strong relationship between the brain ageing process and accelerated brain atrophy, suggesting using brain age prediction models for early diagnosis of neurodegenerative disorders, such as Parkinson’s, Schizophrenia, and Alzheimer’s disease. However, data availability, acquisition protocols diversity and models’ computational complexity remain limiting factors for clinical adoption. This study proposes a low-complexity convolutional neural network (CNN) model that tackles these challenges, focusing on three main aspects: performance accuracy, computational complexity, and adaptability to new, external datasets. We developed a brain-age prediction system using a minimally preprocessed T1-weighted MRI images with a multi-site dataset of healthy individuals covering the whole human lifespan (2251 subjects, age range 6-90 years). We proposed a lighter version of the Simple Fully Convolutional Network (SFCN) that contain only 1.2 million parameters. Computational load was further reduced by cropping the brain images. Finally, we employed transfer learning approach to achieve domain adaptation to external, unseen sites. We demonstrated that leveraging the cropped brain images reduced the computational time for training by 50%, maintaining a comparable accuracy to using the entire brain. The model achieved a Mean Absolute Error (MAE) of 3.557 for the full brain and 4.139 for the cropped images with a Pearson correlation <inline-formula> <tex-math notation="LaTeX">$ r = 0.988 $ </tex-math></inline-formula> between the full and cropped brain predictions when evaluated on the same test set. Domain adaptation of our model to new external data showed a significant improvement in the prediction performance, reducing MAE from 7.219 to 4.750 for full brain images and from 12.107 to 5.770 for the cropped images. This study is the first to demonstrate comparable prediction accuracy using only a small segment of a 3D full brain MRI scan. Our results show that it is feasible to build lightweight CNN models trained on small-scale, heterogeneous datasets and fine-tuned to new external clinical data, making significant steps toward practical clinical application. |
| format | Article |
| id | doaj-art-7e5d2f948d234bb7b5541f5ec405a384 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-7e5d2f948d234bb7b5541f5ec405a3842025-01-15T00:03:02ZengIEEEIEEE Access2169-35362025-01-01136750676310.1109/ACCESS.2025.352652010829597Brain Age Prediction Using a Lightweight Convolutional Neural NetworkFatma Eltashani0https://orcid.org/0000-0003-3165-0009Mario Parreno-Centeno1James H. Cole2Joao Paulo Papa3Fumie Costen4https://orcid.org/0000-0003-1582-1664Electrical and Electronic Engineering Department, The University of Manchester, Manchester, U.K.Comprehensive Cancer Centre, Guy’s Hospital, London, U.K.Centre for Medical Image Computing and Dementia Research Centre, University College London, London, U.K.School of Sciences, São Paulo State University, São Paulo, BrazilElectrical and Electronic Engineering Department, The University of Manchester, Manchester, U.K.Much interest has recently been drawn to brain age prediction due to the significant development in machine learning and image processing techniques. Studies based on brain magnetic resonance images showed a strong relationship between the brain ageing process and accelerated brain atrophy, suggesting using brain age prediction models for early diagnosis of neurodegenerative disorders, such as Parkinson’s, Schizophrenia, and Alzheimer’s disease. However, data availability, acquisition protocols diversity and models’ computational complexity remain limiting factors for clinical adoption. This study proposes a low-complexity convolutional neural network (CNN) model that tackles these challenges, focusing on three main aspects: performance accuracy, computational complexity, and adaptability to new, external datasets. We developed a brain-age prediction system using a minimally preprocessed T1-weighted MRI images with a multi-site dataset of healthy individuals covering the whole human lifespan (2251 subjects, age range 6-90 years). We proposed a lighter version of the Simple Fully Convolutional Network (SFCN) that contain only 1.2 million parameters. Computational load was further reduced by cropping the brain images. Finally, we employed transfer learning approach to achieve domain adaptation to external, unseen sites. We demonstrated that leveraging the cropped brain images reduced the computational time for training by 50%, maintaining a comparable accuracy to using the entire brain. The model achieved a Mean Absolute Error (MAE) of 3.557 for the full brain and 4.139 for the cropped images with a Pearson correlation <inline-formula> <tex-math notation="LaTeX">$ r = 0.988 $ </tex-math></inline-formula> between the full and cropped brain predictions when evaluated on the same test set. Domain adaptation of our model to new external data showed a significant improvement in the prediction performance, reducing MAE from 7.219 to 4.750 for full brain images and from 12.107 to 5.770 for the cropped images. This study is the first to demonstrate comparable prediction accuracy using only a small segment of a 3D full brain MRI scan. Our results show that it is feasible to build lightweight CNN models trained on small-scale, heterogeneous datasets and fine-tuned to new external clinical data, making significant steps toward practical clinical application.https://ieeexplore.ieee.org/document/10829597/Biological age estimationbrain imagingbrain ageingconvolutional neural networkdeep learningmagnetic resonance imaging |
| spellingShingle | Fatma Eltashani Mario Parreno-Centeno James H. Cole Joao Paulo Papa Fumie Costen Brain Age Prediction Using a Lightweight Convolutional Neural Network IEEE Access Biological age estimation brain imaging brain ageing convolutional neural network deep learning magnetic resonance imaging |
| title | Brain Age Prediction Using a Lightweight Convolutional Neural Network |
| title_full | Brain Age Prediction Using a Lightweight Convolutional Neural Network |
| title_fullStr | Brain Age Prediction Using a Lightweight Convolutional Neural Network |
| title_full_unstemmed | Brain Age Prediction Using a Lightweight Convolutional Neural Network |
| title_short | Brain Age Prediction Using a Lightweight Convolutional Neural Network |
| title_sort | brain age prediction using a lightweight convolutional neural network |
| topic | Biological age estimation brain imaging brain ageing convolutional neural network deep learning magnetic resonance imaging |
| url | https://ieeexplore.ieee.org/document/10829597/ |
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