Early Diagnosis of Lung Cancer Using Computer Aided Detection via Lung Segmentation Approach

Early Diagnosis of Lung Cancer Using Computer Aided Detection via Lung Segmentation Approach
  IJETT-book-cover           
  
© 2021 by IJETT Journal
Volume-69 Issue-5
Year of Publication : 2021
Authors : Abhir Bhandary, Ananth Prabhu G, Mustafa Basthikodi, Chaitra K M
DOI :  10.14445/22315381/IJETT-V69I5P213

How to Cite?

Abhir Bhandary, Ananth Prabhu G, Mustafa Basthikodi, Chaitra K M, "Early Diagnosis of Lung Cancer Using Computer Aided Detection via Lung Segmentation Approach," International Journal of Engineering Trends and Technology, vol. 69, no. 5, pp. 85-93, 2021. Crossref, https://doi.org/10.14445/22315381/IJETT-V69I5P213

Abstract
Lung cancer begins in the lungs and leading to the reason of cancer demise amid population in the creation. According to the American Cancer Society, which estimates about 27% of the deaths because of cancer. In the early phase of its evolution, lung cancer does not cause any symptoms usually. Many of the patients have been diagnosed in a developed phase where symptoms become more prominent, that results in poor curative treatment and high mortality rate. Computer Aided Detection systems are used to achieve greater accuracies for the lung cancer diagnosis. In this research exertion, we proposed a novel methodology for lung Segmentation on the basis of Fuzzy C-Means Clustering, Adaptive Thresholding, and Segmentation of Active Contour Model. The experimental results are analysed and presented.

Keywords
Computer Aided Detection, Diagnosis, Fuzzy clustering, Lung cancer, Segmentation, Thresholding

Reference
[1] Tsao, A. S., Scagliotti, G. V., Bunn, P. A., Carbone, D. P., Warren, G. W., Bai, C., de Koning, H. J., Yousaf-Khan, A. U., McWilliams, A., Tsao, M. S., Adusumilli, P. S., Rami-Porta, R., Asamura, H., Van Schil, P. E., Darling, G. E., Ramalingam, S. S., Gomez, D. R., Rosenzweig, K. E., Zimmermann, S., … Pass, H. I., Scientific Advances in Lung Cancer 2015. Journal of Thoracic Oncology, 11(5)(2016) 613–638. https://doi.org/10.1016/j.jtho.2016.03.012
[2] Perez, G., & Arbelaez, P., Automated detection of lung nodules with three-dimensional convolutional neural networks. In J. Brieva, J. D. García, N. Lepore, & E. Romero (Eds.), 13th International Conference on Medical Information Processing and Analysis. SPIE. https://doi.org/10.1117/12.2285954.,(2017).
[3] Muthazhagan, B., Ravi, T., & Rajinigirinath, D., An enhanced computer-assisted lung cancer detection method using content-based image retrieval and data mining techniques. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-020-02123-7.,(2020).
[4] Patel, B., Mishra, D. P. K., & Kolhe, P. A., Lung Cancer Detection on CT Images by using Image Processing. International Journal of Trend in Scientific Research and Development, 2(3)(2018) 2525–2531. https://doi.org/10.31142/ijtsrd11674
[5] Kiran, M., Ahmed, I., Khan, N., & Reddy, A. G., Chest X-ray segmentation using Sauvola thresholding and Gaussian derivatives responses. Journal of Ambient Intelligence and Humanized Computing, 10(10)(2019) 4179–4195. https://doi.org/10.1007/s12652-019-01281-7
[6] Nadiminti, D. V. R. M., & Babu, P. M. S. P., A Novel Integrated Prognosis & Diagnosis System for Lung Cancer Disease Detection using Soft Computing Techniques. International journal of computers & technology, 20(2020) 137–144. https://doi.org/10.24297/ijct.v20i.8844
[7] Na`am, J., Edge Detection on Objects of Medical Image with Enhancement Multiple Morphological Gradient Method. Proceeding of the Electrical Engineering Computer Science and Informatics, 4(1)(2017) https://doi.org/10.11591/eecsi.v4.997
[8] Quadri, A., Shujaee, R., & Khan, N., Review on lung cancer detection using image processing technique. International Journal of Engineering Sciences & Research Technology, 5(2)(2016) 819–823. http://doi.org/10.5281/zenodo.46528
[9] Sandhiya, S., & Palani, U., An effective disease prediction system using incremental feature selection and temporal convolutional neural network. Journal of Ambient Intelligence and Humanized Computing, 11(11)(2020) 5547–5560. https://doi.org/10.1007/s12652-020-01910-6
[10] Shariaty, F., & Mousavi, M., Application of CAD systems for the automatic detection of lung nodules. Informatics in Medicine Unlocked, 15(2019) 100173. https://doi.org/10.1016/j.imu.2019.100173
[11] Perez, G., & Arbelaez, P., Automated lung cancer diagnosis using three-dimensional convolutional neural networks. Medical & Biological Engineering & Computing, 58(8)(2020) 1803–1815. https://doi.org/10.1007/s11517-020-02197-7
[12] Chen, S., Qin, J., Ji, X., Lei, B., Wang, T., Ni, D., & Cheng, J.-Z., Automatic Scoring of Multiple Semantic Attributes with Multi-Task Feature Leverage: A Study on Pulmonary Nodules in CT Images. IEEE Transactions on Medical Imaging, 36(3)(2017) 802–814. https://doi.org/10.1109/tmi.2016.2629462
[13] Ciompi, F., de Hoop, B., van Riel, S. J., Chung, K., Scholten, E. T., Oudkerk, M., de Jong, P. A., Prokop, M., & Ginneken, B., Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box. Medical Image Analysis, 26(1)(2015) 195–202. https://doi.org/10.1016/j.media.2015.08.001
[14] Shelhamer, E., Long, J., & Darrell, T., Fully Convolutional Networks for Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4)(2017) 640–651. https://doi.org/10.1109/tpami.2016.2572683
[15] Basthikodi, M., Faizabadi, A. R., & Ahmed, W., HPC Based Algorithmic Species Extraction Tool for Automatic Parallelization of Program Code. International Journal of Recent Technology and Engineering, 8(2S3)(2019) 1004–1009. https://doi.org/10.35940/ijrte.b1188.0782s319
[16] Xiao, Z., Du, N., Geng, L., Zhang, F., Wu, J., & Liu, Y., Multi-Scale Heterogeneous 3D CNN for False-Positive Reduction in Pulmonary Nodule Detection, Based on Chest CT Images. Applied Sciences, 9(16)(2019) 3261. https://doi.org/10.3390/app9163261
[17] Li, W., Cao, P., Zhao, D., & Wang, J., Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images. Computational and Mathematical Methods in Medicine, (2016) 1–7. https://doi.org/10.1155/2016/6215085
[18] Saba, T. (2020). Recent advancement in cancer detection using machine learning: Systematic survey of decades, comparisons and challenges. Journal of Infection and Public Health, 13(9)(2020) 1274–1289. https://doi.org/10.1016/j.jiph.2020.06.033
[19] Yu, K.-H., Lee, T.-L. M., Yen, M.-H., Kou, S. C., Rosen, B., Chiang, J.-H., & Kohane, I. S., Reproducible Machine Learning Methods for Lung Cancer Detection Using Computed Tomography Images: Algorithm Development and Validation. Journal of Medical Internet Research, 22(8)(2020) e16709. https://doi.org/10.2196/16709
[20] Ajai, A. K., & A, A., A Survey on Artificial Intelligence in Cancer Medical and Nonmedical Datasets. International Journal of Engineering Trends and Technology, 69(3)(2021) 201–210. https://doi.org/10.14445/22315381/ijett-v69i3p231
[21] Kandula, A. R., R, S., & S, N., Performing Uni-variate Analysis on Cancer Gene Mutation Data Using SGD Optimized Logistic Regression. International Journal of Engineering Trends and Technology, 69(2)(2021) 59–67. https://doi.org/10.14445/22315381/ijett-v69i2p209
[22] Chokkadi, S., A Study on various state of the art of the Art Face Recognition System using Deep Learning Techniques. International Journal of Advanced Trends in Computer Science and Engineering,(2019) 1590–1600. https://doi.org/10.30534/ijatcse/2019/84842019
[23] Jianhua Yao, Bliton, J., & Summers, R. M., Automatic Segmentation and Measurement of Pleural Effusions on CT. IEEE Transactions on Biomedical Engineering,(2013) 1834–1840. https://doi.org/10.1109/tbme.2013.2243446
[24] Taha, A. A., & Hanbury, A., Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC medical imaging, 15(2015) 29. https://doi.org/10.1186/s12880-015-0068-x