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Review
. 2020 Sep;39(3):989-998.
doi: 10.1007/s10555-020-09901-x.

Lung cancer identification: a review on detection and classification

Shailesh Kumar Thakur  1 , Dhirendra Pratap Singh  2 , Jaytrilok Choudhary  2
Affiliations
Review

Lung cancer identification: a review on detection and classification

Shailesh Kumar Thakur et al. Cancer Metastasis Rev. 2020 Sep.

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.

Keywords: Benign; Classification; Detection; Lung cancer; Malignant; Nodule.

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