  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.
