An Intelligent System for Lung Cancer Diagnosis Using Fusion of Support Vector Machines and Back Propagation Neural Network

Cancer is the most important cause of death for both men and women. The early detection of cancer can be helpful in curing the disease completely. So the requirement of techniques to detect the occurrence of cancer nodule in early stage is increasing. A disease that is commonly misdiagnosed is lung cancer. Neural Networks (NNs) play a vital role in the medical field in solving various health problems like acute diseases and even other mild diseases. Earlier diagnosis of Lung Cancer saves enormous lives, failing which may lead to other severe problems causing sudden fatal end. Its cure rate and prognosis depends mainly on the early detection and diagnosis of the disease. This thesis provides a Neural Network and SVM model for early detection of lung cancer. The model consists of an input layer, a hidden layer and an output layer. The network is trained with one hidden layer and one output layer by giving twelve inputs. One of the most common forms of medical malpractices globally is an error in diagnosis. By using the fusion of SVM and BPNN we achieved the accuracy of 98 %. The performance simulation is taken place in MATLAB 7.10 environment. The MATLAB has inbuilt Neural Network toolbox and SVM has been implemented using two steps training and testing phases.