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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Different types of Convolutional Neural Networks (CNNs) have been applied todetect cancerous lung nodules from computed tomography (CT) scans. However, thesize of a nodule is very diverse and can range anywhere between 3 and 30millimeters. The high variation of nodule sizes makes classifying them adifficult and challenging task. In this study, we propose a novel CNNarchitecture called Gated-Dilated (GD) networks to classify nodules asmalignant or benign. Unlike previous studies, the GD network uses multipledilated convolutions instead of max-poolings to capture the scale variations.Moreover, the GD network has a Context-Aware sub-network that analyzes theinput features and guides the features to a suitable dilated convolution. Weevaluated the proposed network on more than 1,000 CT scans from the LIDC-LDRIdataset. Our proposed network outperforms state-of-the-art baseline modelsincluding Multi-Crop, Resnet, and Densenet, with an AUC of >0.95. Compared tothe baseline models, the GD network improves the classification accuracies ofmid-range sized nodules. Furthermore, we observe a relationship between thesize of the nodule and the attention signal generated by the Context-Awaresub-network, which validates our new network architecture.

Gated-Dilated Networks for Lung Nodule Classification in CT scans