Brain Age Prediction Using a Lightweight Convolutional Neural Network

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorElectrical and Electronic Engineering Department-
Autor(es): dc.contributorComprehensive Cancer Centre-
Autor(es): dc.contributorDementia Research Centre-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorEltashani, Fatma-
Autor(es): dc.creatorParreno-Centeno, Mario-
Autor(es): dc.creatorCole, James H.-
Autor(es): dc.creatorPaulo Papa, Joao-
Autor(es): dc.creatorCosten, Fumie-
Data de aceite: dc.date.accessioned2025-08-21T16:07:07Z-
Data de disponibilização: dc.date.available2025-08-21T16:07:07Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2024-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/ACCESS.2025.3526520-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/305560-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/305560-
Descrição: dc.descriptionMuch 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 r = 0.988 $ 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.-
Descrição: dc.descriptionThe University of Manchester Electrical and Electronic Engineering Department-
Descrição: dc.descriptionGuy's Hospital Comprehensive Cancer Centre-
Descrição: dc.descriptionUniversity College London Centre for Medical Image Computing Dementia Research Centre-
Descrição: dc.descriptionSão Paulo State University School of Sciences-
Descrição: dc.descriptionSão Paulo State University School of Sciences-
Formato: dc.format6750-6763-
Idioma: dc.languageen-
Relação: dc.relationIEEE Access-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectBiological age estimation-
Palavras-chave: dc.subjectbrain ageing-
Palavras-chave: dc.subjectbrain imaging-
Palavras-chave: dc.subjectconvolutional neural network-
Palavras-chave: dc.subjectdeep learning-
Palavras-chave: dc.subjectmagnetic resonance imaging-
Título: dc.titleBrain Age Prediction Using a Lightweight Convolutional Neural Network-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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