Detection of Lung Cancer Lesions Using 3D Convolutional Neural Networks and Segmentation for Accurate Detection

Authors

  • Rajani Kumari  B. Tech Students, Department of Electronics and Communication Engineering, Sanskrithi School of Engineering, Puttaparthi, Andhra Pradesh, India
  • C. Thanuja  B. Tech Students, Department of Electronics and Communication Engineering, Sanskrithi School of Engineering, Puttaparthi, Andhra Pradesh, India
  • K. Sai Thanvi  B. Tech Students, Department of Electronics and Communication Engineering, Sanskrithi School of Engineering, Puttaparthi, Andhra Pradesh, India
  • K. Lakshmi  B. Tech Students, Department of Electronics and Communication Engineering, Sanskrithi School of Engineering, Puttaparthi, Andhra Pradesh, India
  • U. Lavanya  

DOI:

https://doi.org/10.32628/IJSRST218442

Keywords:

Lung Cancer, Convolutional Neural Network, Tensorflow, CT Scan

Abstract

Lung cancer is a leading cause of death worldwide; it refers to the uncontrolled growth of abnormal cells in the lung. A computed tomography (CT) scan of the thorax is the most sensitive method for detecting cancerous lung nodules. A lung nodule is a round lesion which can be either non-cancerous or cancerous. In the CT, the lung cancer is observed as round white shadow nodules. In existing method, the candidate ROIs shape features are calculated, and some blood vessels are get rid of using rule-based according to shape features; secondly, the remainder candidates gray and texture features are calculated; finally, the shape, gray and texture features are taken as the inputs of the SVM (Support Vector Machine) classifier to classify the candidates. Experimental results show that the rule-based approach has no omission, but the misclassification probability is too large; Hence, in the proposed method the nodules were characterized by the computation of the texture features obtained from the gray level co-occurrence matrix (GLCM) in the wavelet domain and were classified using a SVM with radial basis function in order to classify CT images into two categories: with cancerous lung nodules and without lung nodules. The stages of the proposed methodology to design the CADx system are: 1) Extraction of the region of interest, 2) Wavelet transform, 3) Feature extraction, 4) Attribute and sub-band selection and 5) Classification. The same classification is implemented for the convolution neural networks. The final comparison is done between these two networks based on the accuracy.

References

  1. Diciotti S, Lombardo S, Falchini M, Picozzi G, Mascalchi M. “Automated segmentation refinement of small lung nodules in CT scans by local shape analysis”. IEEE T Bio-Med Eng.vol.58,12,pp:3418-28.doi:10.1109/TBME.2011.2167621. Dec. 2011.
  2. Lee M, Boroczky L, Sungur K, Cann A, Borczuk A, Kawut S, et al.” Computer-aided diagnosis of pulmonary nodules using a two-step approach for feature selection and classifier ensemble construction”. Artif Intell Med. Vol.50,1, pp:43-53. doi: 10.1016/j.artmed.2010.04.011. Sep.2010.
  3. Van de Wouwer G, Scheunders P, Van Dyck D. “Statistical texture characterization from discrete wavelet representations”. IEEE T Image Process. Vol.8,4, pp:592-8. doi: 10.1109/83.753747. 1999.
  4. Singh R, Khare A. ``Fusion of multimodal medical images using daubechies complex wavelet transform: a multiresolution approach”. Inform Fusion. 2014.
  5. Armato S, McLennan G, Bidaut L, McNitt-Gray M, Meyer C. “The lung image database consortium (LIDC) and image Database resource initiative (IDRI)”: a completed reference database of lung nodules on CT scans. Medical Physics vol.38,2, pp:915-931. Jan.2011.
  6. Jing Z, Bin L, Lianfang T. “Lung nodule classification combining rule-based and SVM.” In: Edited by Li K, Proceedings of the IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications: 23–26 September 2010; Changsha, China. Piscataway, NJ: IEEE Computer Society; 2010.
  7. Zhu Y, Tan Y, Hua Y, Wang M, Zhang G, Zhang J. ``Feature selection and performance evaluation of support vector machine (svm)-based classifiers for differentiating benign and malignant pulmonary nodules by computed tomography”. Journal of Digital Imaging vol.23,1, pp:51-65, March 2009;
  8. Chen H, Zhang J, Xu Y, Chen B, Zhang K. “Performance comparison of artificial neural network and logistic regression model for differentiating lung nodules on CT scans”. Journal of Biomedical Science and Engineering, Vol.6 No.11, November 25, 2013.
  9. Nidhi S. Nadkarni, Prof. Sangam Borkar, “Detection of Lung Cancer in CT Images using Image Processing”, Third International Conference on Trends in Electronics and Informatics, IEEE, 2019.
  10. Kumar SA, Ramesh J, Vanathi PT, Gunavathi K.“Robust and automated lung nodule diagnosis from CT images based on fuzzy systems”. In: Edited by Manikandan V, Proceedings of the IEEE International Conference on Process Automation, Control and Computing: 20–22 July 2011; Coimbatore, India. Piscataway, NJ: IEEE Women in Engineering; 2011.
  11. Keshani M, Azimifar Z, Taheripour F, Boostani R. “Lung nodule segmentation and recognition using SVM classifier and active contour modeling”: a complete intelligent system Comput Biol Med. Vol.4,4, pp:287–300. doi: 10.1016/j.compbiomed.2012.12.004.2013.
  12. S.Logesh kumar1, M. Swathy, S. Sathish, J. Sivaraman and M. Rajasekar, “Identification of Lung Cancer Cell using Watershed Segmentation on CT Images”, Indian Journal of Science and Technology, Vol.9,1, DOI:10.17485/ijst/2016/v9i1/85765, January 2016.
  13. Janee Alam1, Sabrina Alam2, Alamgir Hossan3 “Multi-Stage Cancer Detection and Prediction Using Multi-class SVM classifier”, International Journal of Computer Science and Information Technologies, Vol.4, issue 1, pp:39-45.2018.
  14. Kuruvilla J, Gunavathi K. “Lung cancer classification using neural networks for CT images”. Comput Meth Prog Bio. Vol.113, issue 1, Jan.2014.
  15. Gigliarano C, Figini S, Muliere P. “Making classifier performance comparisons when ROC curves intersect”. Comput Stat Data An. Vol. 77, pp:300–12.743:287–300. 2014.

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Published

2021-08-30

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Section

Research Articles

How to Cite

[1]
Rajani Kumari, C. Thanuja, K. Sai Thanvi, K. Lakshmi, U. Lavanya "Detection of Lung Cancer Lesions Using 3D Convolutional Neural Networks and Segmentation for Accurate Detection" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 8, Issue 4, pp.435-439, July-August-2021. Available at doi : https://doi.org/10.32628/IJSRST218442