Categorization & Recognition of Lung Tumor Using Machine Learning Representations

Author(s): Ummadi Janardhan Reddy*, Busi Venkata Ramana Reddy, Boddi Eswara Reddy.

Journal Name: Current Medical Imaging

Volume 15 , Issue 4 , 2019

Become EABM
Become Reviewer

Graphical Abstract:


Abstract:

Background: Lung Cancer is the disease spreading around the world nowadays. Early recognition of lung disease is a difficult task as the cells which cause tumor will grow quickly and the majority of these cells are enclosed with each other. From the beginning of the treatment, tumor detection handling systems which are generally utilized for the diagnosis of lung cancer, recognizable proof of hereditary and ecological elements is imperative in creating a novel technique for lung tumor detection. In different cancers, for example, lung cancer, the time calculated is imperative to find the anomaly issue in target images.

Methods: In this proposed framework, GLCM (Gray Level Co-event Matrix) is utilized for preprocessing of images and to feature extraction procedures to check the condition of the patient whether it is ordinary or irregular. Surface-based elements, for example, GLCM (Gray Level Co-event Matrix) features assume a vital part of remedial image examination which is utilized for the identification of Lung cancer. In the event that lung cancer is effectively distinguished and anticipated in its initial stages, it lessens numerous treatment choices and furthermore, decreases the danger of intrusive surgery and increment survival rate.

Results & Conclusion: The proposed method will efficiently identify the position of the tumor in lungs using the probability framework. This will offer a promising outcome for recognition and diagnosis of lung cancer. In this manuscript, GLCM features are used for the prediction of lung tumor and tests are performed for performance analysis in comparison with the histogram and GLCM features, in which GLCM features are accurate in predicting lung tumor even if it takes more time than histogram features. In this manner, early discovery and probability of lung cancer should assume a crucial task in finding a procedure and furthermore, an increment in the survival rate of the patient. This exploration investigates machine learning systems which consider quality articulation, to perceive cancer or to identify lung cancer.

Keywords: Lung disease, cancer tumors, GLCM, intrusive surgery, machine learning, intrusive surgery.

[1]
Armato SG, Giger ML, Moran CJ, Blackburn JT, Doi K, MacMahon H. Computerized detection of pulmonary nodules on CT scans. Radiographics 1999; 19(5): 1303-11.
[2]
Ruprah TS. Face recognition based on pca algorithm. Int J Comput Sci Inform 2012; 2(1): 221-5.
[3]
Ada RajneetK. Early detection and prediction of lung cancer survival using neural network classifier. IJAIEM 2013; 2(6): 375-83.
[4]
Tao Y, Lu L, Dewan M, et al. Multi-level ground glass nodule detection and segmentation in CT lung images. In: Yang GZ, Hawkes D, Rueckert D, Noble A, Taylor C, Eds. International Conference on Medical Image Computing and Computer-Assisted Intervention. September 20-24, 2009; London: UK. 715-23.
[5]
Sharma D, Jindal G. Computer aided diagnosis system for detection of lungcancer in CT scan images. IJECE 2011; 3(5): 714-8.
[6]
Zhao B, Reeves AP, Yankelevitz D, Henschke CI. Three-dimensional multi-criterion automatic segmentation of pulmonary nodules of helical computed tomography images. Opt Eng 1999; 38(8): 1340-8.
[7]
Villa CH, Anselmo AC, Mitragotri S, Muzykantov V. Red blood cells: supercarriers for drugs, biologicals, and nanoparticles and inspiration for advanced delivery systems. Adv Drug Deliv Rev 2016; 106: 88-103.
[8]
Ganeshan B, Abaleke S, Young RC, Chatwin CR, Miles KA. Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage. Cancer Imaging 2010; 10(1): 137-43.
[9]
Gangotrinathaney Kanakkalyani. Lung cancer detection system on CT images-a survey. IJPRET 2015; 3(9): 848-56.
[10]
Dehmeshki J, Amin H, Valdivieso M, Ye X. Segmentation of pulmonary nodules in thoracic CT scans: A region growing approach. IEEE Trans Med Imaging 2008; 27(4): 467-80.
[11]
Al-tarawneh MS. Lung cancer detection using image processing techniques. Leonardo El J Pract Technol 2012; 11(21): 147-58.
[12]
Yoo Y, Shim H, Yun ID, Lee KW, Lee SU. Segmentation of ground glass opacities by asymmetric multi-phase deformable model. In: Reinhardt JM, Pluim JPW, Eds. Medical Imaging: Image Processing 16 March 2006; San Diego, California, United States; pp. 1-8.
[13]
Blechschmidt RA, Werthschutzky R, Lorcher U. Automated CT image evaluation of the lung: A morphology-based concept. IEEE Trans Med Imaging 2001; 20(5): 434-42.
[14]
Bhat G, Biradar VG, Nalini HS. Artificial Neural Network based Cancer Cell Classification (ANN–C3). Comput Eng Intel Syst 2012; 3(2): 116-9.
[15]
Messay T, Hardie RC, Rogers SK. A new computationally efficient CAD system for pulmonary nodule detection in CT imagery. Med Image Anal 2010; 14(3): 390-406.
[16]
Shi Y, Qi F, Xue Z, et al. Segmenting lung fields in serial chest radiographs using both population-based and patient-specific shape statistics. IEEE Trans Med Imaging 2008; 27(4): 481-94.
[17]
Patil SA, Kuchanur MB. Lung cancer classification using image processing. Int J Eng Innov Technol 2012; 2(3): 37-42.
[18]
Wang Q, Song E, Jin R, et al. Segmentation of lung nodules in computed tomography images using dynamic programming and multidirection fusion techniques1. Acad Radiol 2009; 16(6): 678-88.
[19]
Lingayat NS, Tarambale MR. A computer based feature extraction of lung Nodule in chest x-ray image. Int J Biosci Biochem Bioinform 2013; 3(6): 624-9.
[20]
Okada K, Comaniciu D, Krishnan A. Robust anisotropic Gaussian fitting for volumetric characterization of pulmonary nodules in multislice CT. IEEE Trans Med Imaging 2005; 24(3): 409-23.
[21]
Prasad DV. Lung cancer detection using image processing techniques. IJETT 2013; 3(1): 372-8.
[22]
Hashemi A, Pilevar AH, Rafeh R. Mass detection in lung CT images using region growing segmentation and decision making based on fuzzy inference system and artificial neural network. IJIGSP 2013; 5(6): 16-24.
[23]
Kostis WJ, Reeves AP, Yankelevitz DF, Henschke CI. Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images. IEEE Trans Med Imaging 2003; 22(10): 1259-74.
[24]
Laccetti AL, Pruitt SL, Xuan L, Halm EA, Gerber DE. Early cancer does not adversely affect survival in locally advanced lung cancer: A national SEER-medicare analysis. Lung Cancer 2016; 98: 106-13.
[25]
Larkins DB, Harvey W. Introductory computational science using MATLAB and image processing. Procedia Comput Sci 2010; 1(1): 913-9.
[26]
Bhattacharjee A, Richards WG, Staunton J, et al. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci USA 2001; 98(24): 13790-5.
[27]
Way TW, Chan HP, Goodsitt MM, et al. Effect of CT scanning parameters on volumetric measurements of pulmonary nodules by 3D active contour segmentation: A phantom study. Phys Med Biol 2008; 53(5): 1295.
[28]
Zinoveva O, Zinovev D, Siena SA, Raicu DS, Furst J, Armato SG. A texture-based probabilistic approach for lung nodule segmentation. In: Kamel M, Campilho A, Eds. International Conference Image Analysis and Recognition. June 22-24, 2011; Springer, Berlin: Heidelberg 21-30.
[29]
Sowmiya T, Gopi M, New BM, Thomas RL. Optimization of lung cancer using modern data mining techniques. Int J Engine Res 2014; 3(5): 309-14.
[30]
Diciotti S, Lombardo S, Coppini G, Grassi L, Falchini M, Mascalchi M. The $LoG $ characteristic scale: A consistent measurement of lung nodule size in CT imaging. IEEE Trans Med Imaging 2010; 29(2): 397-409.


Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 15
ISSUE: 4
Year: 2019
Page: [405 - 413]
Pages: 9
DOI: 10.2174/1573405614666180212162727
Price: $58

Article Metrics

PDF: 22
HTML: 3
EPUB: 1
PRC: 1