Lung cancer detection and classification using Support Vector Machine (International Journal of Engineering and Computer Science, 2015)
Lung cancer is that the most significant reason behind cancer death for each man and woman. Early detection is incredibly necessary to reinforce a patient’s likelihood for survival of carcinoma. For early and automatic respiratory organ tumor detection, we have a tendency to purpose a system that relies on textural options. There are 5 main phases concerned within the planned CAD system. They’re image pre-processing, segmentation, feature extraction, classification of carcinoma as benign or malignant. The respiratory organ parenchyma region is segmental as a pre-processing as a result of the tumor resides inside the region. This reduces the search area over that we glance for the tumours, thereby increasing process speed. This additionally reduces the prospect of false identification of tumor. The image pre-processing is done by using fuzzy filter. Segmentation is done by using water shade algorithm, Textural options extracted from the respiratory organ nodules victimisation grey level co-occurrence matrix (GLCM). Then finally for classification, SVM classifier is utilized. This classifier is utilized to classify the nodules as Benign or Malignant.