  COVID-19 has adversely affected humans and societies in different aspects.
Numerous people have perished due to inaccurate COVID-19 identification and,
consequently, a lack of appropriate medical treatment. Numerous solutions based
on manual and automatic feature extraction techniques have been investigated to
address this issue by researchers worldwide. Typically, automatic feature
extraction methods, particularly deep learning models, necessitate a powerful
hardware system to perform the necessary computations. Unfortunately, many
institutions and societies cannot benefit from these advancements due to the
prohibitively high cost of high-quality hardware equipment. As a result, this
study focused on two primary goals: first, lowering the computational costs
associated with running the proposed model on embedded devices, mobile devices,
and conventional computers; and second, improving the model's performance in
comparison to previously published methods (at least performs on par with
state-of-the-art models) in order to ensure its performance and accuracy for
the medical recognition task. This study used two neural networks to improve
feature extraction from our dataset: VGG19 and ResNet50V2. Both of these
networks are capable of providing semantic features from the nominated dataset.
To this end, An alternative network was considered, namely MobileNetV2, which
excels at extracting semantic features while requiring minimal computation on
mobile and embedded devices. Knowledge distillation (KD) was used to transfer
knowledge from the teacher network (concatenated ResNet50V2 and VGG19) to the
student network (MobileNetV2) to improve MobileNetV2 performance and to achieve
a robust and accurate model for the COVID-19 identification task from chest
X-ray images.
