Early Detection of Lung Cancer Using Image Processing and Classification Technique
Dicom , classifier, SVM & feature vector
In this paper, an approach is proposed which uses CT scan images of lungs for the effective diagnosis of lung cancer at a prior stage thus increases the survival rate of patient. The most common sign of lung cancer is the pulmonary nodule. The most crucial and important aspect of image processing is the effective identification of lung cancer nodules. This system first preprocesses the image in dicom format (DCM) for removal of noise and segmentation of the region of interest. Feature vector is then defined by extracting the structural and textural features. In this paper, support vector machine(SVM) classifier is applied to detect lung cancer as well as its severity(whether stage 1 or stage 2)as this algorithm achieves a accuracy of 95.12% which helps to reduce the mortality rate of this deadly disease by taking the remedial actions by patients.
Neha, Dr. Jayant Shekhar. "Early Detection of Lung Cancer Using Image Processing and Classification Technique".INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH ISSN:2321-9939, Vol.3, Issue 2, pp.1290-1294, URL :https://rjwave.org/ijedr/papers/IJEDR1502210.pdf
Volume 3 Issue 2
Pages. 1290-1294
