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Estimating brain age is vital for understanding neurological disorders and the aging process, playing a key role in medical research and treatment. This paper introduces an innovative approach using a three-dimensional residual neural network (3DResNet) enhanced by an attention mechanism. The attention mechanism ensures the model focuses on crucial areas for accurate brain age estimation while preserving growth patterns. Trained on 3D brain scans, including MRI data, the model's operational efficiency is judged based on mean absolute error. Interpretability analysis identifies brain regions that influence age estimation, supporting model trustworthiness and biological insight.

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Attention-Optimized Models for Early Detection of Neurological Aging

  • 会议论文

  • 发表日期：

    2026年

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  • Garima Jain,
  • Shikha Sharma

摘要

Estimating brain age is vital for understanding neurological disorders and the aging process, playing a key role in medical research and treatment. This paper introduces an innovative approach using a three-dimensional residual neural network (3DResNet) enhanced by an attention mechanism. The attention mechanism ensures the model focuses on crucial areas for accurate brain age estimation while preserving growth patterns. Trained on 3D brain scans, including MRI data, the model's operational efficiency is judged based on mean absolute error. Interpretability analysis identifies brain regions that influence age estimation, supporting model trustworthiness and biological insight.