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A Short Survey Work for Lung Cancer Diagnosis Model: Algorithms Utilized, Challenging Issues, and Future Research Trends

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Communication and Intelligent Systems (ICCIS 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 968))

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Abstract

Because of the systemic side effects of the presence of a tumor or due to abnormal results obtained from radiography carried out on the chest, lung cancer is generally identified in suspected people. On the basis of the lung cancer type, the diagnosis approach can be altered. Factors like the location, metastasis presence, size of the tumor, and the cancer type all influence the overall diagnosis and detection process. The most efficient method of detecting lung cancer is staging them into their types. The best approaches for detecting lung cancer are utilized in diagnosis procedures in order to enhance the disease detection sensitivity and to avoid unnecessary invasion techniques. Since lung cancer causes more deaths in both men and women, the efficient diagnosis method for detecting lung cancer has to be known. To overcome all the limitations in conventional lung cancer detection and diagnosis framework, a detailed review of traditional lung cancer diagnosis systems is carried out in this work. In the primary section, the basic steps and procedures involved in the conventional lung cancer detection and diagnosis frameworks are provided. Following this, a detailed survey on the conventional lung cancer diagnosis system is done. A short chronological evaluation is then implemented to evaluate the timeline of the lung cancer diagnosis system. Using literature survey, the methodologies used in conventional works are identified and grouped. Consequently, the datasets adopted for testing and training these systems are studied. The performance measures are used in analyzing the classical lung cancer diagnosis system that is then investigated. Limitations and advantages of conventional lung cancer diagnosis systems are then classified. Finally, the research gaps and the futuristic direction are given.

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Authors and Affiliations

  1. Smt. K. D. Patel Department of Information Technology, Chandubhai S. Patel Institute of Technology (CSPIT), Faculty of Technology and Engineering (FTE), Charotar University of Science and Technology (CHARUSAT), Changa, Gujarat, India

    Nishat Shaikh, Nishat Shaikh & Parth Shah

Authors
  1. Nishat Shaikh
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  2. Parth Shah
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Corresponding author

Correspondence to Nishat Shaikh .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, Rajasthan Technical University, Kota, Rajasthan, India

    Harish Sharma

  2. Department of Electrical and Electronics Engineering, National Institute of Technology Uttarakhand, Srinagar, Uttarakhand, India

    Vivek Shrivastava

  3. Department of Computer Science, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Ashish Kumar Tripathi

  4. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Lipo Wang

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Shaikh, N., Shah, P. (2024). A Short Survey Work for Lung Cancer Diagnosis Model: Algorithms Utilized, Challenging Issues, and Future Research Trends. In: Sharma, H., Shrivastava, V., Tripathi, A.K., Wang, L. (eds) Communication and Intelligent Systems. ICCIS 2023. Lecture Notes in Networks and Systems, vol 968. Springer, Singapore. https://doi.org/10.1007/978-981-97-2079-8_27

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