  Manual analysis and diagnosis of COVID-19 through the examination of Computed
Tomography (CT) images of the lungs can be time-consuming and result in errors,
especially given high volume of patients and numerous images per patient. So,
we address the need for automation of this task by developing a new deep
learning model-based pipeline. Our motivation was sparked by the CVPR Workshop
on "Domain Adaptation, Explainability and Fairness in AI for Medical Image
Analysis", more specifically, the "COVID-19 Diagnosis Competition (DEF-AI-MIA
COV19D)" under the same Workshop. This challenge provides an opportunity to
assess our proposed pipeline for COVID-19 detection from CT scan images. The
same pipeline incorporates the original EfficientNet, but with an added
Attention Mechanism: EfficientNet-AM. Also, unlike the traditional/past
pipelines, which relied on a pre-processing step, our pipeline takes the raw
selected input images without any such step, except for an image-selection step
to simply reduce the number of CT images required for training and/or testing.
Moreover, our pipeline is computationally efficient, as, for example, it does
not incorporate a decoder for segmenting the lungs. It also does not combine
different backbones nor combine RNN with a backbone, as other pipelines in the
past did. Nevertheless, our pipeline still outperforms all approaches presented
by other teams in last year's instance of the same challenge, at least based on
the validation subset of the competition dataset.
