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A Bird’s Eye View Approach on the Usage of Deep Learning Methods in Lung Cancer Detection and Future Directions Using X-Ray and CT Images

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Abstract

This review article provides an overview of recent research on deep learning (DL) methods for identifying and classifying lung nodules in medical images, with a focus on X-ray and CT scans. It encompasses a thorough analysis of studies published in reputed/peer-reviewed journals and international conferences. The review explores various aspects, including the development and implementation of DL models, the use of data augmentation techniques to enhance model performance and the application of transfer learning to adapt existing models to new datasets. The findings highlight the effectiveness of DL techniques in improving accuracy and efficiency in lung nodule detection and classification. Furthermore, these methodologies can be employed to cultivate automated systems that have the potential to aid radiologists in the processes of diagnosis and treatment planning. This review underscores the importance of continued research and development into the present state of DL research about detecting and classifying lung nodules.

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The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Karunya Institute of Technology and Sciences, Coimbatore, 641 114, India, Tamil Nadu

    P. K. Kalkeseetharaman & S. Thomas George

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  1. P. K. Kalkeseetharaman
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All authors contributed to the study’s conception and design. Material preparation, data collection, and analysis were performed by KK, STG. The first draft of the manuscript was written by KK, STG. All authors read and approved the final manuscript.

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Correspondence to S. Thomas George.

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Kalkeseetharaman, P.K., George, S.T. A Bird’s Eye View Approach on the Usage of Deep Learning Methods in Lung Cancer Detection and Future Directions Using X-Ray and CT Images. Arch Computat Methods Eng (2024). https://doi.org/10.1007/s11831-023-10056-5

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