Lung cancer identification: a review on detection and classification

doi:10.1007/s10555-020-09901-x

Paper

Lung cancer identification: a review on detection and classification

Published Jun 9, 2020 · S. K. Thakur, Dhirendra Pratap Singh, Jaytrilok Choudhary

Cancer and Metastasis Reviews

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Abstract

Lung cancer is one of the most common diseases among humans and one of the major causes of growing mortality. Medical experts believe that diagnosing lung cancer in the early phase can reduce death with the illustration of lung nodule through computed tomography (CT) screening. Examining the vast amount of CT images can reduce the risk. However, the CT scan images incorporate a tremendous amount of information about nodules, and with an increasing number of images make their accurate assessment very challenging tasks for radiologists. Recently, various methods are evolved based on handcraft and learned approach to assist radiologists. In this paper, we reviewed different promising approaches developed in the computer-aided diagnosis (CAD) system to detect and classify the nodule through the analysis of CT images to provide radiologists’ assistance and present the comprehensive analysis of different methods.

Study Snapshot

Key takeawayComputer-aided diagnosis (CAD) systems can effectively detect and classify lung nodules in CT images, aiding radiologists in early detection and cancer risk reduction.

PopulationOlder adults (50-71 years)

Sample size24

MethodsObservational

OutcomesBody Mass Index projections

ResultsSocial networks mitigate obesity in older groups.

Lung cancer identification: a review on detection and classification

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