DETECTION OF LUNG CANCER FROM CT-IMAGE USING SVM CLASSIFICATION AND COMPARE THE SURVIVAL RATE OF PATIENTS USING 3D CONVENTIONAL NEURAL NETWORKS ON LUNG NODULE DATA ST

Abstract

Cancer is a quite common and dangerous disease. The various methods of cancer exist in 
the worldwide. Lung cancer is the most typical variety of cancer. The beginning of treatment is 
started by diagnosing CT scan. The risk of death can be minimized by detecting the cancer very 
early. The cancer is diagnosed by computed tomography machine to process further. In this 
paper, the lung nodules are differentiated using the input CT images. The lung cancer nodules 
are classified using support vector machine classifier and the proposed method convolutional 
neural network classifier. Training and predictions using those classifiers are done. The Nodules 
which are grown in the lung cancer are tested as normal and tumor image. The testing of the CT 
images are done using SVM and CNN classifier. Deep learning is always given prominent place 
for the classification process in present years. Especially this type of learning is used in the 
execution of tensor Flow and convolutional neural network method using different deep learning 
libraries.