使用孤立森林技术与残差神经网络（ResNet）进行脑龄预测
Brain Ageing Prediction using Isolation Forest Technique and Residual Neural Network (ResNet)
AI总结：
本文提出基于ResNet101V2并结合孤立森林异常值检测的MRI脑龄预测方法，在ICBM数据集上取得优于其他预训练模型的精度。
摘要：
脑老化是一个复杂且动态的过程，会导致大脑功能和结构发生变化。这些变化可能增加神经退行性疾病和认知衰退的风险。利用神经影像数据进行准确的脑龄估计已成为检测神经退行性早期迹象的必要手段。在此，我们提出一种新颖的深度学习方法，使用残差神经网络101版本2（ResNet101V2）模型从MRI扫描中预测脑龄。为了训练、验证和测试我们提出的模型，我们使用了一个包含2102张图像的大型数据集，这些图像是从国际脑图谱联盟（ICBM）中随机选取的。接下来，我们应用了数据预处理技术，包括图像归一化以及使用孤立森林方法进行异常值检测。然后，我们评估了多种预训练方法（即：MobileNetV2、ResNet50V2、ResNet101V2、Xception）。结果表明，与其他模型相比，ResNet101V2模型具有更高的性能，在使用孤立森林处理之前和之后分别达到了0.9136年和0.8242年的平均绝对误差（MAE）。我们的方法在ICBM数据集上实现了高精度的脑龄估计，并提供了可靠的脑龄预测。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
