Lung Cancer Image- Feature Extraction and Classification using GLCM and SVM Classifier
R. Ankita1, Ch.Usha Kumari2, Mohd Javeed Mehdi3, N. Tejashwini4, T. Pavani5

1R.Ankita , M.Tech Scholar, Embedded Systems, Department of ECE, GRIET, Hyderabad, India.
2Ch. Usha Kumari, Professor, Department of ECE, GRIET, Hyderabad, Telangana, India.
3Mr. Mohd. Javeed Mehdi, Assistant Professor. Department of ECE, GRIET, Hyderabad, Telangana, India.
4N.Tejashwini is M.Tech Scholar , Embedded Systems, Department of ECE, GRIET, Hyderabad, India.
5Dr T. Pavani, Professor, Department of ECE, GRIET, Hyderabad, Telangana, India.

Manuscript received on 24 August 2019. | Revised Manuscript received on 06 September 2019. | Manuscript published on 30 September 2019. | PP: 2211-2215 | Volume-8 Issue-11, September 2019. | Retrieval Number: K20440981119/2019©BEIESP | DOI: 10.35940/ijitee.K2044.0981119
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Abstract: Lung cancer is the second most causing cancer when compared to all the other cancers. According to WHO (World Health Organization) lung cancer contributes about 14 per cent among all the cancers. Therefore, early detection and treatment is very much required. Now-a- days, image processing techniques are playing a major role in early detection of disease which is very helpful in further treatment stages. These techniques help in detecting the abnormality of the tissues-tumor in target cancer images. In this research, the proposed methodology is majorly carried out in five phases. In phase one lung cancer and non-lung cancer, images are collected from the lung cancer database. In phase two preprocessing is done by using the Median filter. Median filter is chosen as it preserves the edges i.e, sharp features are preserved. In Phase three, segmentation of the target image is done using Fuzzy C Means. Fuzzy C Means Clustering is chosen as it gives better performance than K-means Clustering. In phase four, the features are extracted using GLCM (Gray Level Co-occurrence Matrix). GLCM have high discrimination accuracy and less computational speed. In phase five, these extracted features are given to SVM classifier for classification of lung cancer from normal lung. The SVM classier achieved accuracy of 96.7% for detecting and classification of lung cancer.
Keywords: Median filter, FCM (Fuzzy C Means clustering), GLCM (Gray Level Co-occurrence Matrix), , SVM (Support Vector Machine).
Scope of the Article: Classification