Abstract
Lung cancer is the unbridled growth of abnormal cells in the lungs; as the growth of these abnormal cells continues, tumours are formed which obstructs with the natural functioning of the lung. Early cancer diagnoses, combined with treatment and proper medical care, enhances survival and cure rates. This study of lung cancer detection has divided into four stages: a pre-processing stage, image enhancement stage, feature extraction stage, and cancer classification stage. The system thoroughly focuses on detecting lung cancer disease with various image processing and machine learning techniques. The system accepts input in the form of an image called a computerized tomography (CT) scan, which is a medical screening technique used to study and detect lung cancer. This study aims to classify lung cancer into benign and malignant lung cancer using CT scan images. The testing of this system on the given dataset has shown a classification accuracy of 92.37% for determining benign or malignant cancer.
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Kamdar, A., Sharma, V., Sonawane, S., Patil, N. (2022). Lung Cancer Detection by Classifying CT Scan Images Using Grey Level Co-occurrence Matrix (GLCM) and K-Nearest Neighbours. In: Roy, S., Sinwar, D., Perumal, T., Slowik, A., Tavares, J.M.R.S. (eds) Innovations in Computational Intelligence and Computer Vision . Advances in Intelligent Systems and Computing, vol 1424. Springer, Singapore. https://doi.org/10.1007/978-981-19-0475-2_27
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DOI: https://doi.org/10.1007/978-981-19-0475-2_27
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