An exhaustive study on the lung cancer risk models. (5th July 2020)

• Record Type: Journal Article
• Authors: Shanid, Malayil; Anitha, A.
• Is Part Of: International journal of bioinformatics research and applications. Volume 16:Number 2(2020)
• Page Start: 151
• Page End: 172
• Publication Date: 2020-07-05
• Subjects: lung cancer -- computed tomography -- SVM -- support vector machine -- accuracy -- risk assessment -- cancer detection -- mortality rate -- features -- datasets -- research gaps; Bioinformatics -- Periodicals; 570.285
• Languages: English
• ISSNs: 1744-5485

Abstract

One of the critical cancers leading to an upsurging rate of mortality is lung cancer. The Computed Tomography (CT) is the vastly adopted technique for effective cancer detection and risk assessment. The mortality rate and the intrusive surgery can be reduced through the risk assessment of cancer at the earlier stages. Hence, an essential lung cancer detection technique must be modelled for the risk assessment of cancer at the earlier stages. This review paper is made by carrying out a detailed survey on 40 research works presenting the existing lung cancer detection methodologies. Also extensive analysis and discussion is made with respect to the publication year, adopted detection schemes, evaluation metrics, utilised datasets, a simulation tool, accuracy range, and the extracted features. Subsequently, the research gaps and issues of the distinct lung cancer detection schemes are elucidated for directing the researchers to a better contribution of effective cancer risk assessment.