Computer aided diagnosis systems based on brain imaging are an important tool to assist in the diagnosis of Parkinson's disease, whose ultimate goal is the detection by automatic recognizing of patterns that characterize the disease. In recent times Convolutional Neural Networks (CNN) have proved to be amazingly useful for that task. The drawback, however, is that 3D brain images contain a huge amount of information that leads to complex CNN architectures. When these architectures become too complex, classification performances often degrades because the limitations of the training algorithm and overfitting. Thus, this paper proposes the use of isosurfaces as a way to reduce such amount of data while keeping the most relevant information. These isosurfaces are then used to implement a classification system which uses two of the most well-known CNN architectures, LeNet and AlexNet, to classify DaTScan images with an average accuracy of 95.1% and AUC = 97%, obtaining comparable (slightly better) values to those obtained for most of the recently proposed systems. It can be concluded therefore that the computation of isosurfaces reduces the complexity of the inputs significantly, resulting in high classification accuracies with reduced computational burden.

中文翻译：

---

使用基于等值面的特征和卷积神经网络检测帕金森病

基于脑成像的计算机辅助诊断系统是辅助诊断帕金森病的重要工具，其最终目标是通过自动识别表征疾病的模式进行检测。最近，卷积神经网络 (CNN) 已被证明对这项任务非常有用。然而，缺点是 3D 大脑图像包含大量信息，导致复杂的 CNN 架构。当这些架构变得过于复杂时，由于训练算法和过度拟合的限制，分类性能通常会下降。因此，本文建议使用等值面作为减少此类数据量同时保留最相关信息的方法。然后使用这些等值面来实现一个分类系统，该系统使用两个最著名的 CNN 架构 LeNet 和 AlexNet，以平均 95.1% 和 AUC = 97% 对 DaTScan 图像进行分类，获得可比较（略好）的值对于大多数最近提出的系统获得的那些。因此可以得出结论，等值面的计算显着降低了输入的复杂性，从而降低了计算负担，从而提高了分类精度。