Open Access

REVIEW

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

by Salman Ahmed¹, Fazli Subhan^2,3, Mazliham Mohd Su’ud^3,*, Muhammad Mansoor Alam^3,4, 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:

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

Received 15 May 2024; Accepted 13 August 2024; Issue published 22 November 2024

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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Keywords

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