Accurate Brain Age Prediction Through Advanced Preprocessing and 3D ResNet-50 Modeling
Bibliographic record
Abstract
Accurate brain age prediction from structural magnetic resonance imaging (MRI) holds significant potential for advancing our understanding of the aging process and its effects on neural structures.In this paper, a robust preprocessing pipeline and two state-of-the-art 3D convolutional neural network architectures, 3D ResNet-50 and 3D DenseNet-121, were employed to develop and evaluate a brain age prediction model.The preprocessing steps included skull removal, spatial normalization to the Montreal Neurological Institute (MNI) template, and brain tissue segmentation into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF).These steps ensured consistency and accuracy in the input data.The experimental results demonstrated that the 3D ResNet-50 architecture achieved superior performance, with a mean absolute error (MAE) of 3.9 for individuals over 50 years of age, surpassing the MAE of 4.1 achieved by the 3D DenseNet-121 model.These findings validate the efficacy of the proposed preprocessing pipeline and highlight the critical role of tailored deep learning architectures in brain age prediction.Future research could further enhance prediction accuracy by integrating multimodal imaging data and exploring hybrid model architectures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".