  Over the last year, the severe acute respiratory syndrome coronavirus-2
(SARS-CoV-2) and its variants have highlighted the importance of screening
tools with high diagnostic accuracy for new illnesses such as COVID-19. To that
regard, deep learning approaches have proven as effective solutions for
pneumonia classification, especially when considering chest-x-rays images.
However, this lung infection can also be caused by other viral, bacterial or
fungi pathogens. Consequently, efforts are being poured toward distinguishing
the infection source to help clinicians to diagnose the correct disease origin.
Following this tendency, this study further explores the effectiveness of
established neural network architectures on the pneumonia classification task
through the transfer learning paradigm. To present a comprehensive comparison,
12 well-known ImageNet pre-trained models were fine-tuned and used to
discriminate among chest-x-rays of healthy people, and those showing pneumonia
symptoms derived from either a viral (i.e., generic or SARS-CoV-2) or bacterial
source. Furthermore, since a common public collection distinguishing between
such categories is currently not available, two distinct datasets of
chest-x-rays images, describing the aforementioned sources, were combined and
employed to evaluate the various architectures. The experiments were performed
using a total of 6330 images split between train, validation and test sets. For
all models, common classification metrics were computed (e.g., precision,
f1-score) and most architectures obtained significant performances, reaching,
among the others, up to 84.46% average f1-score when discriminating the 4
identified classes. Moreover, confusion matrices and activation maps computed
via the Grad-CAM algorithm were also reported to present an informed discussion
on the networks classifications.

Image and Video Processing;Computer Vision and Pattern Recognition