Classification System for Lung Cancer Nodule Using Machine Learning Technique and CT Images

Nowadays, Lung cancer emerges as one of the most common cancers that adversely affects the people. According to statistics 4,30,000 people are affected by lung cancer. Computed Tomography is the popularly used diagnosis method for lung cancer. In contempt of accurate diagnosis at the early stages, there are some limitations for this method like time consumption and tumour boundaries are unclear. Delta Radiomics is another method used to analyze the image features with respect to time quantitatively for a region of interest and the extracted features are develop to improve the screening of lung cancer. In this paper the features of the cancer nodules are extracted by Delta Radiomics using the machine learning techniques. The Support Vector Machine (SVM) is used to predict the malignancy of the lung cancer nodules. The SVM is capable of analyzing compact features in a lung cancer nodule image and classification of an image is performed effectively for differentiating the various nodules. Therefore, SVM is suggested as the most suitable method for diagnosing and detecting the lung cancer.