论文标题
使用具有深度学习的传感器阵列进行高精度和高精度诊断的多维分析
Multidimensional analysis using sensor arrays with deep learning for high-precision and high-accuracy diagnosis
论文作者
论文摘要
在接下来的几年中，人工智能（AI）将在大多数专业中改变医学的实践。深度学习可以帮助实现更好和更早的问题检测，同时减少诊断错误。通过向深度神经网络（DNN）喂食来自低成本和低精度传感器阵列的数据，我们证明，有可能显着提高测量值的精度和准确性。数据收集是由由32个温度传感器组成的阵列完成的，其中包括16个模拟传感器和16个数字传感器。所有传感器的精度在0.5-2.0 $^\ Circ $ c之间。提取800个向量，覆盖范围从30到45 $^\ circe $ c。为了改善温度读数，我们使用机器学习通过DNN执行线性回归分析。为了最大程度地减少模型的复杂性以最终在本地运行推断，最佳结果的网络仅使用双曲线切线激活函数和ADAM随机梯度下降（SGD）优化器进行三层。使用640个矢量（占数据的80％），用随机选择的数据集对该模型进行训练，并用160个向量（20％）进行了测试。在训练集上使用平均平方错误作为数据和模型预测之间的损耗函数，在训练集上仅达到1.47x10 $^{ - 4} $，在测试集中获得了1.22x10 $^{ - 4} $。因此，我们认为这种吸引人的方法为使用易于可用的超低成本传感器提供了一种新的途径。
In the upcoming years, artificial intelligence (AI) is going to transform the practice of medicine in most of its specialties. Deep learning can help achieve better and earlier problem detection, while reducing errors on diagnosis. By feeding a deep neural network (DNN) with the data from a low-cost and low-accuracy sensor array, we demonstrate that it becomes possible to significantly improve the measurements' precision and accuracy. The data collection is done with an array composed of 32 temperature sensors, including 16 analog and 16 digital sensors. All sensors have accuracies between 0.5-2.0$^\circ$C. 800 vectors are extracted, covering a range from to 30 to 45$^\circ$C. In order to improve the temperature readings, we use machine learning to perform a linear regression analysis through a DNN. In an attempt to minimize the model's complexity in order to eventually run inferences locally, the network with the best results involves only three layers using the hyperbolic tangent activation function and the Adam Stochastic Gradient Descent (SGD) optimizer. The model is trained with a randomly-selected dataset using 640 vectors (80% of the data) and tested with 160 vectors (20%). Using the mean squared error as a loss function between the data and the model's prediction, we achieve a loss of only 1.47x10$^{-4}$ on the training set and 1.22x10$^{-4}$ on the test set. As such, we believe this appealing approach offers a new pathway towards significantly better datasets using readily-available ultra low-cost sensors.
