Top

Wireless Personal Communications

Published in:

30-07-2022

Classifying Lung Cancer as Benign and Malignant Nodule Using ANN of Back-Propagation Algorithm and GLCM Feature Extraction on Chest X-Ray Images

Authors: D. Napoleon, I. Kalaiarasi

Published in: Wireless Personal Communications | Issue 1/2022

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Lung cancer is the most suffering disease which is very difficult to identify in advance and it is not easily cure if the stage of cancer becomes more malignant, the lung cancer is similar like other cancers such as breast cancer, colorectal cancer, brain tumour etc. Now-a-days, there are lot of technologies are developed to predict and treating the diseases, but still have some trouble in detecting the cancer nodule more accurately. Due to increasing in number of patients admitted in clinic, hospitals, etc., doctors cannot able to monitor every patient with high care and they failed to guide their patients with greater attention. Accordingly, the radiologists require a technology named Computer Aided Design (CAD) system for precise recognition and classification of lung nodule where the detected node is cancerous or non-cancerous. In the proposed research, the Chest X-Ray (CXR) images are used as an input image for experimenting the research and image processing techniques has been used to classify the nodule as benign or malignant and executed with greater accuracy in prediction and classification level. In this proposed research work, features were extracted from hasil segmentation image by using Grey Level Co- occurrence Matrix (GLCM) method. The extracted features from image are taken as input data and processed with Artificial Neural Network (ANN) Classifier. The classification and training has been done by Artificial Neural Network with back propagation (ANN-BP) method; therefore, the Artificial Neural Network has competitive and greater in executing the results by comparing with the existing methods of Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM). Therefore, the performance evaluation of Artificial Neural Network has less training time with better accuracy of 87.5%, sensitivity of 97.75% and specificity of 89.75% by classifying the detected nodule as benign or malignant.

previous article Comparative Study on Implementation Performance Analysis of Simulink Models of Cognitive Radio Based GFDM and UFMC Techniques for 5G Wireless Communication

next article Modulation Based Combination of High Level Features Generated from SCCF & Contourlet Transforms for CBIR Applications

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Cancer Statistics. (2020). Report from the National cancer registry programme, India.

Ahmad, W., et al. (2016). Classification of infection and fluid regions using CXR images. In IEEE international conference on DIC: Techniques and applications, pp. 1–5. doi:https://doi.org/10.1109/DICTA.2016.7797020.

Chen, L., et al. (2017). Visual saliency- based method automatic lung regions extraction in chest radiographs. In IEEE 14th international computer conference on wavelet active media technology and information processing (ICCWAMTIP), pp. 162–165. doi:https://doi.org/10.1109/ICCWAMTIP.2017.8301470

Annangi, P., et al. (2010). A region based active contour method for X-ray lung segmentation using prior shape and low-level features. In IEEE International symposium on biomedical imaging: From nano to macro, pp. 892–895.

Abdul Hamid, H. (2014). Image segmentation for lung region in chest X-ray images using edge detection and morphology. In IEEE international conference on control system, computing and engineering (ICCSCE 2014), pp. 46–51. doi:https://doi.org/10.1109/ICCSCE.2014.7072687

Duryea, J., & Boone, J. M. (1995). A fully automated algorithm for the segmentation of lung fields on digital chest radiographic images. Medical Physics, 22(2), 183–191. https://doi.org/10.1118/1.597539CrossRef

Ginneken, B. V., et al. (2002). Active shape model segmentation with optimal features. IEEE Transactions on Medical Imaging, 21(8), 924–933.CrossRef

Aggarwal, T., et al. (2015). Feature extraction and LDA based classification of lung nodules in chest CT scan images. In International conference on advances in computing, communications and informatics (ICACCI). doi:https://doi.org/10.1109/ICACCI.7275773.

Jin. X., et al. (2016). Pulmonary nodule detection based on CT images using convolution neural network. In 9th International symposium on computational intelligence and design (ISCID). doi:https://doi.org/10.1109/ISCID.2016.1053

10.

Roy, T., et al. (2015). Classification of lung image and nodule detection using fuzzy inference system. In International conference on computing, communication & automation. doi:https://doi.org/10.1109/CCAA.2015.7148560.

11.

Rendon-Gonzalez, E., et al. (2016). Automatic Lung nodule segmentation and classification in CT images based on SVM. In 9th International Kharkiv symposium on physics and engineering of microwaves, millimetre and sub-millimetre waves (MSMW). doi:https://doi.org/10.1109/MSMW.2016.7537995

12.

Khan, S. A., et al. (2019). Effective and reliable framework for lung nodules detection from CT scan images. Scientific Reports, 9(1), 4989. https://doi.org/10.1038/s41598-019-41510-9CrossRef

13.

Bhat, G., et al. (2012). Artificial neural network based cancer cell classification (ANN-C3). Computer Engineering and Intelligent Systems, 3(2), 7.

14.

Nasser, I. M., et al. (2019). Lung cancer detection using artificial neural network. International Journal of Engineering and Information Systems (IJEAIS), 3, 17–23.

15.

Ginneken, B. V., et al. (2001). Computer-aided diagnosis in chest radiography: A survey. IEEE, Transactions on Medical Imaging. https://doi.org/10.1109/42.974918CrossRef

16.

Gupta, B., et al. (2014). Lung cancer detection using curvelet transform and neural network. International Journal of Computer Applications, 86(1), 15–17.CrossRef

17.

Sankar, K., et al. (2014). Gray coefficient mass estimation based image segmentation technique for lung cancer detection using gabor filters. Journal of Theoretical and Applied Information Technology, 66(2), 638–644.

18.

Yametkar, A. M. (2014). Lung cancer detection and classification by using bayesian classifier. In Proceedings of IRF international conference, pp. 7–13.

19.

Brindha, A. A., et al. (2016). Lung cancer detection using SVM algorithm and optimization techniques. Journal of Chemical and Pharmaceutical Sciences, 9(4), 2016.

20.

Guo, W., et al. (2012). A computerized scheme for lung nodule detection in multiprotection chest radiography. Medical Physics, 39(4), 2001–2012. https://doi.org/10.1118/1.3694096.PMID:22482621CrossRef

21.

Suzuki, K., et al. (2003). Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography. Medical Physics, 30(7), 1602–1617. https://doi.org/10.1118/1.1580485,PMID:12906178CrossRef

22.

Farag, A., et al. (2017). Feature fusion for lung nodule classification. International Journal of Computer Assisted Radiology and Surgery, 12(10), 1809–1818. https://doi.org/10.1007/s11548-017-1626-1CrossRef

23.

Zhao, B., et al. (2003). Automatic detection of small lung nodules on CT utilizing a local density maximum algorithm. Journal of Applied Clinical Medical Physics, 4(3), 248–260. https://doi.org/10.1120/jacmp.v4i3.2522CrossRef

24.

Kobayashi, H., et al. (2017). A method for evaluating the performance of computer-aided detection of pulmonary nodules in lung cancer CT screening: detection limit for nodule size and density. The British Journal of Radiology, 90(1070), 20160313. https://doi.org/10.1259/bjr.20160313CrossRef

25.

Goo, J. M., et al. (2011). Computer-aided diagnosis for evaluating lung nodules on chest CT: The current status and perspective. Korean Journal of Radiology, 12(2), 145–155. https://doi.org/10.3348/kjr.2011.12.2.145MathSciNetCrossRef

26.

Lo, S. B., et al. (2018). Computer-aided detection of lung nodules on CT with a computerized pulmonary vessel suppressed function. American Journal of Roentgenology, 210(3), 480–488. https://doi.org/10.2214/AJR.17.18718CrossRef

27.

Candemir, S., et al. (2019). A review on lung boundary detection in chest X-rays. International Journal of Computer Assisted Radiology and Surgery, 14(4), 563–576. https://doi.org/10.1007/s11548-019-01917-1CrossRef

28.

Xie, Y., et al. (2019). Knowledge based method with deep learning for classification of benign, malignant nodule on chest CT image. IEEE Transaction on Medical Imaging. https://doi.org/10.1109/TMI.2018.2876510CrossRef

29.

Chouhan, V., et al. (2020). A novel transfer learning based approach for pneumonia detection in chest X-ray images. Applied Sciences, 10(2), 559. https://doi.org/10.3390/app10020559CrossRef

30.

Zotina, A. et al. (2016). Lung boundary detection for chest X-ray images classification based on GLCM and probabilistic neural networks. In 23rd International conference on knowledge-based and intelligent information & engineering systems. Procedia Computer Science, 159, 1439–1448.

31.

Junaedi, I., et al. (2019). Tuberculosis detection in chest x-ray images using optimized gray level co-occurrence matrix features. International Conference on Information and Communications Technology (ICOIACT). https://doi.org/10.1109/ICOIACT46704.2019.8938584CrossRef

32.

Nitin, S., et al. (2013). A computer based feature extraction of lung nodule in chest x-ray image. International Journal of Bioscience, Biochemistry and Bioinformatics. https://doi.org/10.7763/IJBBB.2013.V3.289CrossRef

33.

Kain, N. K. (2018). Understanding of Multilayer perceptron (MLP). http://www.medium.com.

34.

Bogdan, M., et al. (2010). Improved computation for Levenberg– Marquardt training. IEEE Transactions on Neural Networks, 21(6), 930–937.CrossRef

35.

A Gentle Introduction to Cross-Entropy for Machine Learning. http://www.Machinelearningmastery.com.

Title: Classifying Lung Cancer as Benign and Malignant Nodule Using ANN of Back-Propagation Algorithm and GLCM Feature Extraction on Chest X-Ray Images
Authors: D. Napoleon
I. Kalaiarasi
Publication date: 30-07-2022
Publisher: Springer US
Published in: Wireless Personal Communications / Issue 1/2022
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI: https://doi.org/10.1007/s11277-022-09594-1

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 1/2022

Implementation of Extra Efficient Bandwidth Utilization Dynamic Bandwidth Allocation Algorithm to Support Differentiated Service Classes in XGPON

Sea Turtle Foraging and Hydrozoan Optimization Algorithm-based NLOS Node Positioning Scheme for Reliable Data Dissemination in Vehicular Ad hoc Networks

Weak Harmonic Signal Detecting in Chaotic Noise Based on Empirical Likelihood Ratio

Two-Element Quad-Band MIMO Antenna Employing Meandered Arm Printed Inverted–F Antenna

Linear Algebraic Theory for Designing the Bus Topology to Enhance the Data Transmission Process

A Hybrid Analytic Model for the Effective Prediction of Different Stages in Chronic Kidney Ailments