Citation

Abdullahi, Kabiru and Ramakrishnan, Kannan and Ali, Aziah (2025) Deep Learning Techniques for Lung Cancer Diagnosis with Computed Tomography Imaging: A Systematic Review for Detection, Segmentation, and Classification. Information, 16 (6). p. 451. ISSN 2078-2489

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Abstract

Background/Objectives: Lung cancer is a major global health challenge and the leading cause of cancer-related mortality, due to its high morbidity and mortality rates. Early and accurate diagnosis is crucial for improving patient outcomes. Computed tomography (CT) imaging plays a vital role in detection, and deep learning (DL) has emerged as a transformative tool to enhance diagnostic precision and enable early identification. This systematic review examined the advancements, challenges, and clinical implications of DL in lung cancer diagnosis via CT imaging, focusing on model performance, data variability, generalizability, and clinical integration. Methods: Following the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we analyzed 1448 articles published between 2015 and 2024. These articles are sourced from major scientific databases, including the Institute of Electrical and Electronics Engineers (IEEE), Scopus, Springer, PubMed, and Multidisciplinary Digital Publishing Institute (MDPI). After applying stringent inclusion and exclusion criteria, we selected 80 articles for review and analysis. Our analysis evaluated DL methodologies for lung nodule detection, segmentation, and classification, identified methodological limitations, and examined challenges to clinical adoption. Results: Deep learning (DL) models demonstrated high accuracy, achieving nodule detection rates exceeding 95% (with a maximum false-positive rate of 4 per scan) and a classification accuracy of 99% (sensitivity: 98%). However, challenges persist, including dataset scarcity, annotation variability, and population generalizability. Hybrid architectures, such as convolutional neural networks (CNNs) and transformers, show promise in improving nodule localization. Nevertheless, fewer than 15% of the studies validated models using multicenter datasets or diverse demographic data. Conclusions: While DL exhibits significant potential for lung cancer diagnosis, limitations in reproducibility and real-world applicability hinder its clinical translation. Future research should prioritize explainable artificial intelligence (AI) frameworks, multimodal integration, and rigorous external validation across diverse clinical settings and patient populations to bridge the gap between theoretical innovation and practical deployment.

Item Type:	Article
Uncontrolled Keywords:	Lung cancer diagnosis, deep learning, lung nodule detection, segmentation, classification, computed tomography, convolutional neural networks (CNNs), vision transformer (ViTs)
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	29 Jul 2025 01:09
Last Modified:	31 Jul 2025 07:07
URII:	http://shdl.mmu.edu.my/id/eprint/14329

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