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

In this paper, we studied extensively on different deep learning basedmethods to detect melanoma and skin lesion cancers. Melanoma, a form ofmalignant skin cancer is very threatening to health. Proper diagnosis ofmelanoma at an earlier stage is crucial for the success rate of complete cure.Dermoscopic images with Benign and malignant forms of skin cancer can beanalyzed by computer vision system to streamline the process of skin cancerdetection. In this study, we experimented with various neural networks whichemploy recent deep learning based models like PNASNet-5-Large,InceptionResNetV2, SENet154, InceptionV4. Dermoscopic images are properlyprocessed and augmented before feeding them into the network. We tested ourmethods on International Skin Imaging Collaboration (ISIC) 2018 challengedataset. Our system has achieved best validation score of 0.76 forPNASNet-5-Large model. Further improvement and optimization of the proposedmethods with a bigger training dataset and carefully chosen hyper-parametercould improve the performances. The code available for download athttps://github.com/miltonbd/ISIC_2018_classification

Automated Skin Lesion Classification Using Ensemble of Deep Neural Networks in ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection Challenge