Transfer Learning for the Efficient Detection of COVID-19 from
  Smartphone Audio Data
Mattia Giovanni Campana Franca Delmastro Elena Pagani
Disease detection from smartphone data represents an open research challenge
in mobile health (m-health) systems. COVID-19 and its respiratory symptoms are
an important case study in this area and their early detection is a potential
real instrument to counteract the pandemic situation. The efficacy of this
solution mainly depends on the performances of AI algorithms applied to the
collected data and their possible implementation directly on the users' mobile
devices. Considering these issues, and the limited amount of available data, in
this paper we present the experimental evaluation of 3 different deep learning
models, compared also with hand-crafted features, and of two main approaches of
transfer learning in the considered scenario: both feature extraction and
fine-tuning. Specifically, we considered VGGish, YAMNET, and
L\textsuperscript{3}-Net (including 12 different configurations) evaluated
through user-independent experiments on 4 different datasets (13,447 samples in
total). Results clearly show the advantages of L\textsuperscript{3}-Net in all
the experimental settings as it overcomes the other solutions by 12.3\% in
terms of Precision-Recall AUC as features extractor, and by 10\% when the model
is fine-tuned. Moreover, we note that to fine-tune only the fully-connected
layers of the pre-trained models generally leads to worse performances, with an
average drop of 6.6\% with respect to feature extraction. %highlighting the
need for further investigations. Finally, we evaluate the memory footprints of
the different models for their possible applications on commercial mobile
devices.