English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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 basedon manual and automatic feature extraction techniques have been investigated toaddress this issue by researchers worldwide. Typically, automatic featureextraction methods, particularly deep learning models, necessitate a powerfulhardware system to perform the necessary computations. Unfortunately, manyinstitutions and societies cannot benefit from these advancements due to theprohibitively high cost of high-quality hardware equipment. As a result, thisstudy focused on two primary goals: first, lowering the computational costsassociated with running the proposed model on embedded devices, mobile devices,and conventional computers; and second, improving the model's performance incomparison to previously published methods (at least performs on par withstate-of-the-art models) in order to ensure its performance and accuracy forthe medical recognition task. This study used two neural networks to improvefeature extraction from our dataset: VGG19 and ResNet50V2. Both of thesenetworks are capable of providing semantic features from the nominated dataset.To this end, An alternative network was considered, namely MobileNetV2, whichexcels at extracting semantic features while requiring minimal computation onmobile and embedded devices. Knowledge distillation (KD) was used to transferknowledge from the teacher network (concatenated ResNet50V2 and VGG19) to thestudent network (MobileNetV2) to improve MobileNetV2 performance and to achievea robust and accurate model for the COVID-19 identification task from chestX-ray images.

Designing an Improved Deep Learning-based Model for COVID-19 Recognition in Chest X-ray Images: A Knowledge Distillation Approach