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

Monkeypox is a rare disease that raised concern among medical specialistsfollowing the convi-19 pandemic. It's concerning since monkeypox is difficultto diagnose early on because of symptoms that are similar to chickenpox andmeasles. Furthermore, because this is a rare condition, there is a knowledgegap among healthcare professionals. As a result, there is an urgent need for anovel technique to combat and anticipate the disease in the early phases ofindividual virus infection. Multiple CNN-based pre-trained models, includingVGG-16, VGG-19, Restnet50, Inception-V3, Densnet, Xception, MobileNetV2,Alexnet, Lenet, and majority Voting, were employed in classification in thisstudy. For this study, multiple data sets were combined, such as monkeypox vschickenpox, monkeypox versus measles, monkeypox versus normal, and monkeypoxversus all diseases. Majority voting performed 97% in monkeypox vs chickenpox,Xception achieved 79% in monkeypox against measles, MobileNetV2 scored 96% inmonkeypox vs normal, and Lenet performed 80% in monkeypox versus all.

A Comparative Analysis of CNN-Based Pretrained Models for the Detection and Prediction of Monkeypox