• Shradha Chandrashekhar Fule
• Ruhina Quazi

Abstract: For early detection of diseases image processing techniques are most commonly used. A study shows that it can improve accuracy, sensitivity and specificity for early detection of lung cancer through image processing techniques. Collected CT (Computed Tomography) scan image of the lungs are pre-processed and the Region of Interest (ROI) is segmented. The proposed system consist of pre-processing, segmentation, features extraction and image analysis from segmented images. Features such as entropy, co-relation, energy, variance and homogeneity are extracted using a GLCM (Gray level Co-occurrence Matrix) with classification effected by means of an SVM (Support Vector Machine). This Classification identifies whether these CT images are normal or abnormal. The Lung Image Database Consortium dataset (LIDC) has been used for collection of normal and abnormal images of lung for training and testing purpose for this study.

Pre-processing; Segmentation ; Thresholding ; Clustering; Features extraction; GLCM (Gray level Co-occurrence Matrix); Normal; Abnormal.

"“Study On Biomedical Image Processing For Detection of Lung Cancer Tumor”", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 1, page no.777-781, January-2019, Available :http://www.jetir.org/papers/JETIR1901A99.pdf

"“Study On Biomedical Image Processing For Detection of Lung Cancer Tumor”", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 1, page no. pp777-781, January-2019, Available at : http://www.jetir.org/papers/JETIR1901A99.pdf