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A Survey of Lung Nodules Detection and Classification from CT Scan Images

Salman Ahmed1, Fazli Subhan2,3, Mazliham Mohd Su’ud3,*, Muhammad Mansoor Alam3,4, Adil Waheed5

1 Faculty of Computing and Technology, IQRA University, Islamabad, 44000, Pakistan
2 Faculty of Engineering and Computer Science, National University of Modern Languages, Islamabad, 44000, Pakistan
3 Faculty of Computing and Informatics, Multimedia University, Cyberjaya, 63100, Malaysia
4 Faculty of Computing, Riphah International University, Islamabad, 44000, Pakistan
5 Department of Information Sciences, Division of Sciences and Technology, University of Education, Lahore, 54770, Pakistan

* Corresponding Author: Mazliham Mohd Su’ud. Email: email

Computer Systems Science and Engineering 2024, 48(6), 1483-1511. https://doi.org/10.32604/csse.2024.053997

Abstract

In the contemporary era, the death rate is increasing due to lung cancer. However, technology is continuously enhancing the quality of well-being. To improve the survival rate, radiologists rely on Computed Tomography (CT) scans for early detection and diagnosis of lung nodules. This paper presented a detailed, systematic review of several identification and categorization techniques for lung nodules. The analysis of the report explored the challenges, advancements, and future opinions in computer-aided diagnosis CAD systems for detecting and classifying lung nodules employing the deep learning (DL) algorithm. The findings also highlighted the usefulness of DL networks, especially convolutional neural networks (CNNs) in elevating sensitivity, accuracy, and specificity as well as overcoming false positives in the initial stages of lung cancer detection. This paper further presented the integral nodule classification stage, which stressed the importance of differentiating between benign and malignant nodules for initial cancer diagnosis. Moreover, the findings presented a comprehensive analysis of multiple techniques and studies for nodule classification, highlighting the evolution of methodologies from conventional machine learning (ML) classifiers to transfer learning and integrated CNNs. Interestingly, while accepting the strides formed by CAD systems, the review addressed persistent challenges.

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APA Style
Ahmed, S., Subhan, F., Su’ud, M.M., Alam, M.M., Waheed, A. (2024). A survey of lung nodules detection and classification from CT scan images. Computer Systems Science and Engineering, 48(6), 1483-1511. https://doi.org/10.32604/csse.2024.053997
Vancouver Style
Ahmed S, Subhan F, Su’ud MM, Alam MM, Waheed A. A survey of lung nodules detection and classification from CT scan images. Comput Syst Sci Eng. 2024;48(6):1483-1511 https://doi.org/10.32604/csse.2024.053997
IEEE Style
S. Ahmed, F. Subhan, M.M. Su’ud, M.M. Alam, and A. Waheed, “A Survey of Lung Nodules Detection and Classification from CT Scan Images,” Comput. Syst. Sci. Eng., vol. 48, no. 6, pp. 1483-1511, 2024. https://doi.org/10.32604/csse.2024.053997



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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