Abstract
The normal cause of death among people all over the world is the lung malignancy. Early recognition of lung disease can explore the possibility of perseverance. From the previous years, it is figured that tomography has picked up its fame in location of lung malignancy. The use of image processing has expanded step by step. This outcome in new developments helps in diagnosing infection precisely. Predictions by radiologist in order to find lung malignancy may not be accurate in general with huge volume of images. This paper proposes an effective lung malignant growth discovery and prediction utilizing custom selective segmentation techniques with SVM classifier. Our proposed method indicates the precision of lung malignancy identification with improved accuracy in classification.
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Ashwini, S.S., Kurian, M.Z., Nagaraja, M. (2022). Lung Cancer Detection and Prediction Using Customized Selective Segmentation Technique with SVM Classifier. In: Shetty, N.R., Patnaik, L.M., Nagaraj, H.C., Hamsavath, P.N., Nalini, N. (eds) Emerging Research in Computing, Information, Communication and Applications. Lecture Notes in Electrical Engineering, vol 790. Springer, Singapore. https://doi.org/10.1007/978-981-16-1342-5_4
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DOI: https://doi.org/10.1007/978-981-16-1342-5_4
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