PROPOSED METHODLung is an important organ in human body that performs vital functions for every second. Hence we need to consider the main lung abnormalities in detection, diagnosis and also treatment of detected abnormality if it is possible. Also lung diseases are said to be the main reason for death around the world. The main reasons behind the lung diseases are inhaling chemicals, inhaling dusts , or even due to bacterial action. According to a survey done on Indian people approximately 70275 were affected by major lung diseases , among which 63759 people were not in a condition to be curedand caused their deaths.
We propose a novel method to detect lung abnormalities in a efficient manner. The parameters are evaluated for feature extraction are Haarlick features, No.of black pixels, grid features and haar wavelets, For classification hybrid feed forward network with nave bayes classifier is used.MODELINGTo detect the lung cancer from the given image a novel classification method is used. Initially the image is preprocessed using wiener filter.
Then the image features are extracted. Then the extracted features are given into the classifier inorder to detect whether the patient affected by cancer or not.PRIMARY OBJECTIVE To detect the lung cancer in a efficient manner. To extract the features of the image effectively. To improve the classification accuracy. To give the noise free solution.PERFORMANCE MEASUREMENTThe performance of the proposed method is evaluated based on the following factors: Accuracy Mean square error Sensitivity SpecificityCONCLUSION In this paper we survey different techniques for lung cancer detection system. Detecting cancer at early stage prove to be vital as the mortality rate is abruptly increasing annually. Lung cancer can be detected by identifying affected nodules at early stage. This paper has given a brief review on recent developments in lung cancer detection methods. Various techniques have been used in the lung cancer detection methods to improve the efficiency of cancer detection. Each method has its own uniqueness, advantages and limitations. The popular classifiers used for lung nodule detection schemes are also presented. REFERENCES Gindi,A. M., Al Attiatalla, T. A., & Sami, M.M. (2014) A Comparative Study for Comparing Two Feature Extraction Methods and Two Classifiers in Classification of Earlystage Lung Cancer Diagnosis of chest x-ray images. Journal of American Science, 10(6): 13-22.  Suzuki, K., Kusumoto, M., Watanabe, S. I., Tsuchiya, R., & Asamura, H. (2006) Radiologic classification of small adenocarcinoma of the lung: radiologic-pathologic correlation and its prognostic impact, The Annals of Thoracic Surgery. 81(2): 413-419.  Xiuhua,G., Tao, S., & Zhigang, L.(2011) Prediction Models for Malignant Pulmonary Nodules Based-on Texture Features of CT Image. In Theory and Applications of CT Imaging and Analysis. DOI: 10.5772/14766. Ms. Twinkal Patel, Asst. Professor Mr. Vimal Nayak, Hybrid Approach For Feature Extraction of Lung Cancer Detection, Proceedings of the 2nd International Conference on Inventive Communication and Computational Technologies (ICICCT 2018) IEEE Xplore Compliant – Part Number: CFP18BAC-ART; ISBN:978-1-5386-1974-2. Tiantian Fang A Novel Computer-Aided Lung Cancer Detection Method Based on Transfer Learning from GoogLeNet and Median Intensity Projections, De-Ming Wong, Chen-Yu Fang, Li-Ying Chen, Chen-I Chiu, Ting-I Chou, Cheng-Chun Wu, Shih-Wen Chiu and Kea-Tiong Tang, Development of a Breath Detection Method Based E-nose System for Lung Cancer Identification, Proceedings of IEEE International Conference on Applied System Innovation 2018 IEEE ICASI 2018- Meen, Prior & Lam (Eds). Qing Wu and Wenbing Zhao, Small-Cell Lung Cancer Detection Using a Supervised Machine Learning Algorithm, 978-1-5386-2941-3/17 $31.00 © 2017 IEEE DOI 10.1109/ISCSIC.2017.22.Janee Alam1 , Sabrina Alam2 , Alamgir Hossan, Multi-Stage Lung Cancer Detection and Prediction Using Multi-class SVM Classifier. N. Hadavi Md. Jan Nordin, Ali Shojaeipour, Lung Cancer Diagnosis Using CT-Scan Images Based on Cellular Learning Automata , IEEE International Conference on Computer and Information Sciences (ICCOINS), 3-5 June 2014. A.Amutha, Dr.R.S.D.Wahidabanu, Lung Tumor Detection and Diagnosis in CT scan Images, International conference on Communication and Signal Processing, April 3-5, “IEEE 2013, India, DOI: 10.1109/iccsp.2013.6577228, pp. 1108-1112.  Hongmei Mi, Petitjean C, Dubray B, Vera, P, Su Ruan, Automatic lung tumor segmentation on PET images based on random walks and tumor growth model, Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium, April 29 – May 2, DOI: 10.1109/ISBI.2014.6868136, pp. 1385-1388. Yuhua Gu, VirendraKumar, LawrenceO.Hall, DmitryB.Goldgof, Ching-YenLi, Rene Korn, Claus Bendtsen, EmmanuelRiosVelazquez, AndreDekker, HugoAerts, PhilippeLambin, XiuliLi, Jie Tian, RobertA.Gatenby, RobertJ.Gillies, Automated delineation of lung tumors from CT images using a single click ensemble segmentation approach, Volume 46, Issue 3, March 2013, ©Elsevier 2012, pp. 692″702.  Dandil E, Cakiroglu M, Eksi Z, Ozkan M, Kurt O.K, Canan A, Artificial Neural Network-Based Classification System for Lung Nodules on Computed Tomography Scans, Soft Computing and Pattern Recognition (SoCPaR), 2014 6th International Conference , Aug 11-14, “IEEE2014,DOI:10.1109/SOCPAR.2014.7008037, pp. 382-386.  A.R.Talebpour, H.R.Hemmati, M.Zarif Hosseinian, Automatic Lung Nodules Detection In Computed Tomography Images Using Nodule Filtering And Neural Networks, The 22nd Iranian Conference on Electrical Engineering (ICEE 2014), May 20-22, “IEEE 2014, Shahid Beheshti University, DOI: 10.1109/IranianCEE.2014.6999847, pp. 1883-1887.  Anam Tariq, M.Usman Akram, M. Younus Javed,Lung Nodule Detection in CT Images using Neuro Fuzzy Classifier, Computational Intelligence in Medical Imaging (CIMI), April 16-19, 2013 “IEEE Fourth International Workshop, DOI:10.1109/CIMI.2013.6583857, ISSN:2326-991X, pp. 49-53.  Hui Cui, Xiuying Wang, Michael Fulham, David Dagan Feng, Prior Knowledge Enhanced Random Walk for Lung Tumor Segmentation from Low-Contrast CT Images, 35th Annual International Conference of the IEEE EMBS, Osaka, Japan, July 3 – 7, 2013 “IEEE, DOI:10.1109/EMBC.2013.6610937, ISSN:1557-170X, pp. 6071-6074. A. Kulkarni ,A. Panditrao, Classification of lung cancer stages on CT scan images using image processing, IEEE,International Conference on Advanced Communication Control and Computing Technologies (ICACCCT), 2014. Fatma Taher, Naoufel Werghi and Hussain Al-Ahmad, Rule Based Classification of Sputum Images for Early Lung Cancer Detection, 978-1-5090-0246-7/15/$31.00 ©2015 IEEE. BIPIN NAIR B J ,JEEVAKUMAR A, ANJU K J, Tobacco Smoking Induced Lung Cancer Prediction By LC-MicroRNAs Secondary Structure Prediction And Target Comparison, 978-1-5090-4307-1/17/$31.00 ©2017 IEEE. Lilik Anifah, Haryanto, Rina Harimurti, Zaimah Permatasari, Puput Wanarti Rusimamto, Adam Ridiantho Muhamad, Cancer Lungs Detection on CT Scan Image Using Artificial Neural Network Backpropagation Based Gray Level Coocurrence Matrices Feature, 978-1-5386-3172-0/17/S31.00© 2017 IEEE. S.Kalaivani1 Pramit Chatterjee 2 Shikhar Juyal 3 Rishi Gupta4, Lung Cancer Detection Using Digital Image Processing and Artificial Neural Networks, 978-1-5090-5686-6/17/$31.00 ©2017 IEEE. Nastaran Emaminejad, Wei Qian, Yubao Guan, Maxine Tan, Yuchen Qiu, Hong Liu, and Bin Zheng, Fusion of Quantitative Image and Genomic Biomarkers to Improve Prognosis Assessment of Early Stage Lung Cancer Patients, 0018-9294 (c) 2015 IEEE. Wasudeo Rahane, Himali Dalvi, Yamini Magar, Anjali Kalane, Satyajeet Jondhale, Lung Cancer Detection Using Image Processing and Machine Learning HealthCare. K. T. Navya, Feroz Hussain Shaik, G. Bhanushankar Reddy, Segmentation of Lung Vessels Using Radon Transform, 978-1-5386-0615-5/17/$31.00 ©2017 IEEE. R. P. Petersen and D. H. Harpole •Computed Tomography Screening for the Early Detection of Lung Cancer, The Journal of the National Comprehensive Cancer Network, Vol. 4 pp.1-4, 2006.  Schilham, A.M.R., Van Ginneken, B. and Loog, M. (2006), A computer-aided diagnosis system for detection of lung nodules in chest radiographs with an evaluation on a public database, Med.Image Anal., Vol. 10 No. 2, pp. 247-58. Dr.G.R.Jothi lakshmi.,Dr. Arun Raaza , Dr. Y. Sreenivasa Varma , Dr. V. Rajendran , R. Guru Nirmal Raj 2018 ,A review of characteristic study of micro calcification using son mammogram images, International Journal of Engineering & Technology, 7 (2.33) (2018) 290-294 Dr.G.R.Jothi lakshmi., E. Gopinathan 2015 Mamogram enhancement using quadratic adaptive volterra filter- A comparative analysis in spatial and frequency domain,ARPN Journal of Engineering and Applied Sciences .