Abstract
Lung cancer or lung carcinoma is one of the major reasons for non-accidental death in the world with a high fatality rate in both men and women. The major cause of lung cancer is wrong lifestyle choices such as consumption of beedi, cigarette, and hukka. Lung cancer is broadly categorized as small cell lung carcinoma (SCLC) and non-small cell lung carcinoma (NSCLC). It is very difficult to detect lung cancer well in advance. However, technological advances in medical imaging have resulted in the diagnosis and prediction of various stages of lung cancer by analyzing CT scans. In the present paper, a constructive review of the existing approaches for lung nodule detection and classification using machine learning approaches is presented. Authors have analyzed the articles published in the last decade to access the current status of the research in the field of lung cancer classification. The survey study concluded that the involvement of optimization approaches to improve the feature extraction and segmentation stage has been involved in recent years. Further, it is observed that the integration of the neural network architecture has become the first choice of numerous researchers for lung cancer classification.
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Shamas, S., Panda, S.N., Sharma, I. (2023). Review on Lung Nodule Segmentation-Based Lung Cancer Classification Using Machine Learning Approaches. In: Gupta, M., Ghatak, S., Gupta, A., Mukherjee, A.L. (eds) Artificial Intelligence on Medical Data. Lecture Notes in Computational Vision and Biomechanics, vol 37. Springer, Singapore. https://doi.org/10.1007/978-981-19-0151-5_24
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DOI: https://doi.org/10.1007/978-981-19-0151-5_24
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