Brain age prediction using improved twin SVR

Ganaie, M. A.; Tanveer, M.; Beheshti, Iman

doi:10.1007/s00521-021-06518-1

Cited by 7 publications

(2 citation statements)

References 47 publications

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“…Predicting brain age from magnetic resonance imaging (MRI) volumes using deep learning has become a popular research topic recently [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ]; see Tanveer et al [ 14 ] for a recent review. More traditional machine learning methods such as regression (often using different features such as the size of different brain regions) have also been used for predicting brain age [ 15 , 16 , 17 ]. If there is a large difference between the predicted brain age and the biological age of a patient, one can suspect that some disease is present and the difference is therefore an important biomarker [ 4 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Efficient Brain Age Prediction from 3D MRI Volumes Using 2D Projections

Jönemo,

Akbar,

Kämpe

et al. 2023

Brain Sciences

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Using 3D CNNs on high-resolution medical volumes is very computationally demanding, especially for large datasets like UK Biobank, which aims to scan 100,000 subjects. Here, we demonstrate that using 2D CNNs on a few 2D projections (representing mean and standard deviation across axial, sagittal and coronal slices) of 3D volumes leads to reasonable test accuracy (mean absolute error of about 3.5 years) when predicting age from brain volumes. Using our approach, one training epoch with 20,324 subjects takes 20–50 s using a single GPU, which is two orders of magnitude faster than a small 3D CNN. This speedup is explained by the fact that 3D brain volumes contain a lot of redundant information, which can be efficiently compressed using 2D projections. These results are important for researchers who do not have access to expensive GPU hardware for 3D CNNs.

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Section: Introductionmentioning

confidence: 99%

Efficient Brain Age Prediction from 3D MRI Volumes Using 2D Projections

Jönemo,

Akbar,

Kämpe

et al. 2023

Brain Sciences

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“…In a separate study [ 10 ], various machine learning algorithms, such as Support Vector Regression and Binary Decision Tree, were evaluated to determine their efficacy in predicting brain age. Researchers have conducted investigations on multiple regression algorithms, including Relevance Vector Regression and Twin Support Vector Regression, in order to estimate brain age using various imaging modalities [ 5 , 11 , 12 ]. In another study, the Gaussian Process Regression algorithm is utilized for brain age estimation and mortality as well [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Brain age gap estimation using attention-based ResNet method for Alzheimer’s disease detection

Aghaei,

Ebrahimi Moghaddam

2024

Brain Inf.

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This study investigates the correlation between brain age and chronological age in healthy individuals using brain MRI images, aiming to identify potential biomarkers for neurodegenerative diseases like Alzheimer's. To achieve this, a novel attention-based ResNet method, 3D-Attention-Resent-SVR, is proposed to accurately estimate brain age and distinguish between Cognitively Normal (CN) and Alzheimer’s disease (AD) individuals by computing the brain age gap (BAG). Unlike conventional methods, which often rely on single datasets, our approach addresses potential biases by employing four datasets for training and testing. The results, based on a combined dataset from four public sources comprising 3844 data points, demonstrate the model's efficacy with a mean absolute error (MAE) of 2.05 for brain age gap estimation. Moreover, the model's generalizability is showcased by training on three datasets and testing on a separate one, yielding a remarkable MAE of 2.4. Furthermore, leveraging BAG as the sole biomarker, our method achieves an accuracy of 92% and an AUC of 0.87 in Alzheimer's disease detection on the ADNI dataset. These findings underscore the potential of our approach in assisting with early detection and disease monitoring, emphasizing the strong correlation between BAG and AD.

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Energy-Based Least Squares Projection Twin SVM

Ganaie¹,

Tanveer²

2022

Lecture Notes in Electrical Engineering

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Brain age prediction using improved twin SVR

Cited by 7 publications

References 47 publications

Efficient Brain Age Prediction from 3D MRI Volumes Using 2D Projections

Efficient Brain Age Prediction from 3D MRI Volumes Using 2D Projections

Brain age gap estimation using attention-based ResNet method for Alzheimer’s disease detection

Energy-Based Least Squares Projection Twin SVM

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