Identification of Efficient Patterns for the Detection of Lung Cancer

Journal: GRENZE International Journal of Computer Theory and Engineering
Authors: K Guna Shekar, Pancham Bopanna D, C Jayvardhan Chetty, Basavaraj V Hiremath, S C Prasannakumar
Volume: 3 Issue: 4
Grenze ID: 01.GIJCTE.3.4.79 Pages: 533-537

Abstract

Lung cancer is one of the most occurring cancers among both men and women [1]. Detection of cancer at the early stage is the only method to improve the survival rate. CT images of lung are helpful in the diagnosis of lung cancer. Doctor analyses the CT image and predicts the presence of cancer nodule. This manual detection can also result in false detection. So in order to overcome this problem a computerized method for cancer detection is needed. Image processing technique can be used for this purpose. By using the following image processing techniques lung cancer detection can be developed further. Lung cancer detection system has three steps to detect the presence of cancer nodule in lung. Preprocessing stage, segmentation and feature extraction stage. Pre-processing step includes image enhancement using Gaussian filter and image segmentation. Enhanced CT image of lung is then fed to segmentation phase. From the segmented output, features are extracted. By using these extracted features the lung is differentiated as normal lung or cancerous lung. And further the cancerous tumor can be differentiated as Benign and Malignant.

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