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

Cancer is the second leading cause of death worldwide. Lung cancer possesses the highest mortality, with non-small cell lung cancer (NSCLC) being its most prevalent subtype of lung cancer. Despite gradual reduction in incidence, approximately 585720 new cancer patients were diagnosed in 2014, with majority from low-and-middle income countries (LMICs). Limited availability of diagnostic equipment, poorly trained medical staff, late revelation of symptoms and classification of the exact lung cancer subtype and overall poor patient access to medical providers result in late or terminal stage diagnosis and delay of treatment. Therefore, the need for an economic, simple, fast computed image-processing system to aid decisions regarding staging and resection, especially for LMICs is clearly imminent. In this study, we developed a preliminary program using MATLAB that accurately detects cancer cells in CT images of lungs of affected patients, measures area of region of interest (ROI) or tumor mass and helps determine nodal spread. A preset value for nodal spread was used, which can be altered accordingly.

Preliminary Detection and Analysis of Lung Cancer on CT images using MATLAB: A Cost-effective Alternative