Abstract
The categorization and identification of lung disorders in medical imageries are made easier by recent advances in deep learning (DL). As a result, various studies using DL to identify lung illnesses were developed. This study aims to analyze different publications that have been contributed to in order to recognize lung cancer. This literature review examines the many methods for detecting lung cancer. It analyzes several segmentation models that have been used and reviews different research papers. It examines several feature extraction methods, such as those using texture-based and other features. The investigation then concentrates on several cancer detection strategies, including “DL models” and machine learning (ML) models. It is possible to examine and analyze the performance metrics. Finally, research gaps are presented to encourage additional investigation of lung detection models.
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