用ResNet101V2和孤立森林检测，精准预测脑龄。
Brain Ageing Prediction using Isolation Forest Technique and Residual Neural Network (ResNet)
- 结合残差网络与孤立森林剔除异常影像，提升模型鲁棒性。
- 预测误差低至0.8242年，优于MobileNetV2等其他模型。
- 适合神经退行性疾病早期筛查，尤其关注脑老化研究者。
脑衰老是复杂动态过程，导致大脑功能与结构改变，增加神经退行性疾病与认知衰退风险。利用神经影像数据准确估计脑龄对早期发现神经退变至关重要。本文提出一种新方法，基于深度学习模型ResNet101V2，从MRI扫描中预测脑龄。模型在来自国际脑图谱联盟（ICBM）的2102张图像上训练、验证与测试。通过图像归一化及孤立森林方法进行异常值检测进行数据预处理。对比了多种预训练模型（MobileNetV2、ResNet50V2、ResNet101V2、Xception），结果表明ResNet101V2性能最优，使用孤立森林前后预测平均绝对误差（MAE）分别为0.9136年和0.8242年。该方法在ICBM数据集上实现高精度脑龄估计，具有可靠预测能力。
原文摘要 · Abstract (English)
Brain aging is a complex and dynamic process, leading to functional and structural changes in the brain. These changes could lead to the increased risk of neurodegenerative diseases and cognitive decline. Accurate brain-age estimation utilizing neuroimaging data has become necessary for detecting initial signs of neurodegeneration. Here, we propose a novel deep learning approach using the Residual Neural Network 101 Version 2 (ResNet101V2) model to predict brain age from MRI scans. To train, validate and test our proposed model, we used a large dataset of 2102 images which were selected randomly from the International Consortium for Brain Mapping (ICBM). Next, we applied data preprocessing techniques, including normalizing the images and using outlier detection via Isolation Forest method. Then, we evaluated various pre-trained approaches (namely: MobileNetV2, ResNet50V2, ResNet101V2, Xception). The results demonstrated that the ResNet101V2 model has higher performance compared with the other models, attaining MAEs of 0.9136 and 0.8242 years for before and after using Isolation Forest process. Our method achieved a high accuracy in brain age estimation in ICBM dataset and it provides a reliable brain age prediction.
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