• Prerana Prajapati
• Vedika Hande
• Aarti Ingale
• Sanjeev Dwivedi

Image processing techniques are widely used in several medical problems for image enhancement in the detection phase to support the early medical treatment. In this research, we aim to improve quality and accuracy of early detection of lung cancer through a combination of image processing techniques and machine learning. The Cancer Imaging Archive (TCIA) dataset has been used for training and testing purpose where DICOM is the primary format used for image storage .Classification is done using SVM (Support Vector Machine) classifier which identifies whether the CT image is cancerous and non-cancerous. Before training the classifier, we are performing image processing techniques on CT images such as converting the image into HU (Hounsfield Unit) scale to get the binary image of lungs followed by nodule segmentation where nodules are detected within the lungs. Further, features are extracted from CT images using GLCM (Gray Level Co-Occurrence Matrix) .The enhanced image is then given to the classifier to provide accurate results. As an enhancement, we have also performed Early Stage Detection for reducing the growing cancer burden. MATLAB image processing toolbox based has been used for the implementation on the CT scan images.

Nodules Detection, Image Processing, Classification, Machine Learning

" Lung Cancer Detection and Classification Using SVM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.60-64, May 2019, Available :http://www.jetir.org/papers/JETIRCS06015.pdf

" Lung Cancer Detection and Classification Using SVM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp60-64, May 2019, Available at : http://www.jetir.org/papers/JETIRCS06015.pdf