Lung cancer is a common occurrence type in a population and one amonglethal cancers. Recently, out of
several research presented by diverse health agencies; it is obvious that the fatality ratio is rising due
todelayeddiagnosis of lung cancer. Hence, an artificial intelligence-based diagnosis is required to find out the onset of
lung nodule micro-calcification, which may support the doctors and radiologists to accurately predict it through image
processing methods. In this paper, a novel technique is proposed to identify the nodule micro-calcification pattern by
using its physical features. The physical features that considered are the reflection coefficients and mass densities of
the binned CT image of lung. The physical features measurements reiteratesonce again the existence of malignant
nodule. Then, by applying the methods of thresholding and in interpolation of physical features, a three-dimensional
(3D) projected image of the region of interest (ROI) is achieved in respect of physical dimensions. Thus, the nodule
size is calculated from 3D projection. This concept is used to verify how best in classification with 100 malignant
images (the nodule presence) and 10 normal images (the nodule absence). Apart size measurement, the proposed
method supports SVM classifier to act for excellentclassification from normal and malignantinput imagesby just using
two physical features. The classifier exhibited an accuracy of 98%.
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Acknowledgements
We would like to thank Reviewers for taking the time and effort necessary to review the manuscript. We sincerely appreciate all valuable comments and suggestions, which helped us to improve the quality of the manuscript.
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Aaron Maurer
Aaron Maurer
Information Technology, College of Engineering, University of Missouri.
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Cite this article
Aaron Maurer, “An Early Prediction of Lung Cancer using CT Scan Images”, Journal of Computing and Natural Science, vol.1, no.2, pp. 039-044, April 2021. doi: 10.53759/181X/JCNS202101008.
