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

The main aim of this work is to propose a novel Computer-aided detection (CAD) system based on a Contextual clustering combined with region growing for assisting radiologists in early identification of lung cancer from computed tomography(CT) scans. Instead of using conventional thresholding approach, this proposed work uses Contextual Clustering which yields a more accurate segmentation of the lungs from the chest volume. Following segmentation GLCM and LBP features are extracted which are then classified using three different classifiers namely Random forest, SVM and k-NN.

Implementation Of Classification Methods For Diagnosis Of Lung Cancer nodules In CT Images