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

Background: Skin cancer is one of the widely seen cancer worldwide andautomatic classification of skin cancer can be benefited dermatology clinicsfor an accurate diagnosis. Hence, a machine learning-based automatic skincancer detection model must be developed. Material and Method: This researchinterests to overcome automatic skin cancer detection problem. A colored skincancer image dataset is used. This dataset contains 3297 images with twoclasses. An automatic multilevel textural and deep features-based model ispresented. Multilevel fuse feature generation using discrete wavelet transform(DWT), local phase quantization (LPQ), local binary pattern (LBP), pre-trainedDarkNet19, and DarkNet53 are utilized to generate features of the skin cancerimages, top 1000 features are selected threshold value-based neighborhoodcomponent analysis (NCA). The chosen top 1000 features are classified using the10-fold cross-validation technique. Results: To obtain results, ten-foldcross-validation is used and 91.54% classification accuracy results areobtained by using the recommended pyramidal hybrid feature generator and NCAselector-based model. Further, various training and testing separation ratios(90:10, 80:20, 70:30, 60:40, 50:50) are used and the maximum classificationrate is calculated as 95.74% using the 90:10 separation ratio. Conclusions: Thefindings and accuracies calculated are denoted that this model can be used indermatology and pathology clinics to simplify the skin cancer detection processand help physicians.

New pyramidal hybrid textural and deep features based automatic skin cancer classification model: Ensemble DarkNet and textural feature extractor