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 analysis of lung diseases at the early stage is a major need in medical field to overcome the patients' severity. Thus, in the proposed work, Support Vector Machine (SVM) based classification method is adopted for precise classification of lung cancer, solid or aerosol Deposition, liquid and semi-liquid Deposition. Initially, the gathered data are pre-processed for image transformation. Image binning is performed to divide the images into several sub-regions, and threshold setting is done for effective segmentation. To evaluate the cell size and infected areas, Region of Interest (ROI) extraction has performed. The physical characteristics encompassing reflection coefficient, mass density and impedance are estimated to achieve effective performance. The prediction and classification of four lung diseases are effectively classified using SVM classifier. The simulation tool used for evaluating the performance is MATLAB. The performance of different physical characteristics shows that the proposed method performs better in classification.

the imaging science journal

An early prediction of lung cancer, solid, liquid and semi-liquid deposition and its classification through measurement of physical characteristics using CT scan images