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Deep learning applications for lung cancer diagnosis: A systematic review

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Abstract

Lung cancer has been one of the most prevalent disease in recent years. According to the research of this field, more than 200,000 cases are identified each year in the US. Uncontrolled multiplication and growth of the lung cells result in malignant tumour formation. Recently, deep learning algorithms, especially Convolutional Neural Networks (CNN), have become a superior way to automatically diagnose disease. The purpose of this article is to review different models that lead to different accuracy and sensitivity in the diagnosis of early-stage lung cancer and to help physicians and researchers in this field. The main purpose of this work is to identify the challenges that exist in lung cancer based on deep learning. The survey is systematically written that combines regular mapping and literature review to review 32 conference and journal articles in the field from 2016 to 2021. In this work, after a complete analysis and review of the articles, the questions raised in the articles have been answered. This research work provides a more comprehensive review compared to previous published review articles in this research area. Furthermore, it includes recent studies and state of the art research works systematically.

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Acknowledgements

The authors would like to thank you Dr. Armin Chitizade for reviewing the manuscript and Dr. Ghasem Naghib for general guidance through this research.

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Authors and Affiliations

  1. Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran

    Seyed Hesamoddin Hosseini & Reza Monsefi

  2. Department of Software Engineering, Islamic Azad University, Mashhad Branch, Mashhad, Iran

    Shabnam Shadroo

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  1. Seyed Hesamoddin Hosseini
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Correspondence to Shabnam Shadroo.

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Hosseini, S.H., Monsefi, R. & Shadroo, S. Deep learning applications for lung cancer diagnosis: A systematic review. Multimed Tools Appl 83, 14305–14335 (2024). https://doi.org/10.1007/s11042-023-16046-w

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