Detection of Distant Lung Cancer using Enhanced Transductive Support Vector Machines

Abstract

Background: Cancer disease is the second largest disease after heart-attack in the world. Cancer is an abnormal growth of normal cell. Cancer is classified based on the cell type where it is mainly affected .There are different types of cancer like blood cancer, brain cancer, small intestine cancer, lung cancer, liver cancer etc. According to ICMR, among 1.27 billion Indian populations, the incidence of cancer is 70 – 90% per 100,000 populations and 70% of cancer is identified in the last stage accounting for high mortality. Though there are hundred form of cancer, the prognosis of bronchogenic carcinoma (lung cancer) is very poor because it can be identified only at a final stage. The beginning tumors are not more dangerous but the malignant tumors are more risky which spread to further portions of the body over blood stream or the lymph vessels. Prognosis and remedy is the biggest provocations in cancer for the medical field and physicians in the past few years. The CT scans support the doctors to detect cancer at early stage. When cancer is prognoses at benign stage, millions of human life across the world gets saved every year. Method: Noise in the CT scan input image is reduced by traditional adaptive median filtering and segmented by Region Based Neural Networks to extract a region of interest. To reduce the unwanted texture and noises and to detect wide-ranged images, Improved Canny Edge detector is implemented. The clinical characteristics of the patient were included as a feature reference. The considered features in clinical characteristics are status of patient smoking ,age of the patient, classification of tumor and T, N staging. Feature selection using Improved Glowworm Swarm Optimization and Classification Enhanced Transductive Support Vector Machines (ETSVM) is utilized to diagnose the distant metastasis of lung cancer. Result: Experimental results shows that ETSVM and Improved Glowworm Swarm Optimization achieved the best performance with an accuracy 90.7% and sensitivity 94.7%.