Citation

Thillaigovindhan, Senthil Kumar and Anaam, Elham Abdulwahab and Palanichamy, Naveen and Haw, Su-Cheng and Jayaram, Jayapradha (2025) A Systematic Review on Effectiveness and Contributions of Machine Learning and Deep Learning Methods in Lung Cancer Diagnosis and Classifications. International Journal of Computing and Digital Systems, 17 (1). pp. 1-12. ISSN 2210-142X

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Abstract

In the present scenario of people’s health, lung tumour is the major reason of cancer-related losses, and its death rates are steadily rising. Several researchers have developed automated techniques for quickly and accurately predicting the development of cancer cells using medical imaging and machine learning techniques. As advances in computerized systems have been made, deep learning techniques have been thoroughly investigated to aid in understanding the results of computer-aided diagnosis (CADx) and computer-aided detection (CADe) in computed tomography (CT), magnetic resonance imaging (MRI), and X-ray for the identification of lung cancer. To provide a thorough review of the deep learning methods created for lung cancer diagnosis and detection, this study is being done. The present study offers an articulation of deep learning and machine learning methods and aims for applications in the diagnosis of lung cancer as well as an analysis of the advancements made in the techniques explored. In order to detect and screen for lung cancer, two main deep learning techniques are used in this study: analysis and categorization across the internal and external organizational environment and its markets. The benefits and drawbacks of the deep learning models that are currently in use will also be covered. Deep Learning technologies can deliver accurate and efficient computerassisted lung tumor detection and diagnosis, as shown by the subsequent analysis of the scan data. This study ends with a description of potential future studies that might enhance the use of deep learning to the creation of computer-assisted lung cancer detection systems.

Item Type:	Article
Uncontrolled Keywords:	Lung Cancer, Diagnosis, Classification, Medical Imaging, Machine Learning, Deep Learning and Analysis.
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:	06 Mar 2025 02:21
Last Modified:	06 Mar 2025 02:21
URII:	http://shdl.mmu.edu.my/id/eprint/13596

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