Lung Cancer Diagnosis by Hybrid Support Vector Machine

A machine learning based classification technique to diagnose lung CT scan images as cancerous or noncancerous is proposed in this paper. Lung cancer is regarded as one of the major fatal disease among the population throughout the world. Early diagnosis of lung cancer can be an important factor which can decrease the death rate among people. In large medical organizations manual inspection of CT scan, MRI images etc. puts a lot of workload on doctors and radiologist. An effective diagnosis technique can really reduce their efforts. CT (Computerized Tomography) scan images are used in medical field to analyze various parts of body. Grey scale CT scan images are used here as dataset, image preprocessing and feature of images are used. SVM classifiers are used for diagnosis. The main objective of this paper is to improve accuracy rate for lung cancer diagnosis by designing a hybrid SVM.