An Automated Lung Cancer Detection Using Soft Computing –A Review
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Abstract
Lung cancer is a disease that causes deaths worldwideto reduce the burden of the radiologists, a review has been
done to understand the performance of the machine learning[ML]model for the detection of cancer in the lung nodule.The
performance outcomes metrics such as sensitivity, specificity,accuracy, receiver operator characteristic. [ROC] curve and the
area under the curve [AUC] are evaluated using the attributes, viz., age factor, CT chest scans, lung nodule, lung cancer,
deep learning, ensemble and classic methods for inclusion earliest and the attributes such as age factor,positron emission
tomography [PET] hybrid scans, chest X-ray [CXR],and genomics for exclusion crises.
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