Hybrid Deep Learning Method for Detection of Liver Cancer

  • Sunita P. Deshmukh Department of Electronics and Telecommunication, GHRU Amaravati, Amaravati, India
  • Dharmaveer Choudhari Department of Electronics and Telecommunication, GHRU Amaravati, Amaravati, India
  • Shankar Amalraj Department of Electronics and Telecommunication, GHRU Amaravati, Amaravati, India
  • Pravin N. Matte Department of Electronics and Telecommunication, GHRU Amaravati, Amaravati, India

Abstract

Liver disease refers to any liver irregularity causing its damage. There are several kinds of liver ailments. Benign growths are rarely life threatening and can be removed by specialists. Liver malignant tumor is leading causes of cancer death. Identifying malignant growth tissue is a troublesome and tedious task. There is significantly less information and statistical analysis presented related to cholangiocarcinoma and hepatoblastoma. This research focuses on the image analysis of these two types of cancer. The framework’s performance is evaluated using 2871 images, and a dual hybrid model is used to accomplish superb exactness. The aftereffects of both neural networks are sent into the result prioritizer that decides the most ideal choice for image arrangement. The relevance of elements appears to address the appropriate imaging rules for each class, and feature maps matching the original picture voxel features. The significance of features represents the most important imaging criteria for each class. This deep learning system demonstrates the concept of illuminating elements of a pre-trained deep neural network’s decision-making process by an examination of inner layers and the description of attributes that contribute to predictions.

Keywords

liver cancer detection, deep learning, fully convolutional neural network, hybrid approach, discrete wavelet transform (DWT),

References

1. H. Alahmer, A. Ahmed, Computer-aided classification of liver lesions from CT images based on multiple ROI, Procedia Computer Science, 90: 80–86, 2016, doi: 10.1016/j.procs.2016.07.027.
2. M.A. Al-Shabi, A novel CAD system for detection and classification of liver cirrhosis using support vector machine and artificial neural network, International Journal of Computer Science and Network Security, 19(8): 18–23, 2019, http://paper.ijcsns.org/07_book/201908/20190804.pdf.
3. S.M. Anwar, M. Majid, A. Qayyum, M. Awais, M. Alnowami, M.K. Khan, Medical image analysis using convolutional neural networks: A review, Journal of Medical Systems, 42(11): 226, 2018, doi: 10.1007/s10916-018-1088-1.
4. F. Bray, J. Ferlay, I. Soerjomataram, R.L. Siegel, L.A. Torre, A. Jemal, Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, Cancer Journal for Clinicians, 68(6): 394–424, 2018, doi: 10.3322/caac.21492.
5. A. Brunetti, L. Carnimeo, G.F. Trotta, V. Bevilacqua, Computer-assisted frameworks for classification of liver, breast and blood neoplasias via neural networks: A survey based on medical images, Neurocomputing, 335: 274–298, 2019, doi: 10.1016/j.neucom.2018.06.080.
6. C.C. Chang et al., Computer-aided diagnosis of liver tumors on computed tomography images, Computer Methods and Programs in Biomedicine, 145: 45–51, 2017, doi: 10.1016/j.cmpb.2017.04.008.
7. E.L. Chen, P.C. Chung, C.L. Chen, H.M. Tsai, C.I. Chang, An automatic diagnostic system for CT liver image classification, IEEE Transactions on Biomedical Engineering, 45(6): 783–794, 1998, doi: 10.1109/10.678613.
8. A. Das, U.R. Acharya, S.S. Panda, S. Sabut, Deep learning based liver cancer detection using watershed transform and Gaussian mixture model techniques, Cognitive Systems Research, 54: 165–175, 2019, doi: 10.1016/j.cogsys.2018.12.009.
9. X. Dong, Y. Zhou, L. Wang, J. Peng, Y. Lou, Y. Fan, Liver cancer detection using hybridized fully convolutional neural network based on deep learning framework, IEEE Access (Special Section on Deep Learning Algorithms for Internet of Medical Things), 8: 129889–129898, 2020, doi: 10.1109/ACCESS.2020.3006362.
10. H. Fujita et al., An introduction and survey of computer-aided detection/diagnosis (CAD), [in:] International Conference on Future Computer, Control and Communication, pp. 200–205, IEEE, 2010, http://www.fjt.info.gifu-u.ac.jp/publication/668.pdf.
11. M. Gletsos, S.G. Mougiakakou, G.K. Matsopoulos, K.S. Nikita, A.S. Nikita, D. Kelekis, A computer-aided diagnostic system to characterize CT focal liver lesions: Design and optimization of a neural network classifier, IEEE Transactions on Information Technology in Biomedicine, 7(3): 153–162, 2003, doi: 10.1109/titb.2003.813793.
12. M. Gletsos, S.G. Mougiakakou, G.K. Matsopoulos, K.S. Nikita, A.S. Nikita, D. Kelekis, Classification of hepatic lesions from CT images using texture features and neural networks, [in:] 2001 Proceedings of the 23rd Annual EMBS International Conference, Istanbul, Turkey, pp. 2748–2751, 2001.
13. F. Gorunescu, S. Belciug, M. Gorunescu, R. Badea, Intelligent decision-making for liver fibrosis stadialization based on tandem feature selection and evolutionary-driven neural network, Expert Systems with Applications, 39(17): 12824–12832, 2012, doi: 10.1016/j.eswa.2012.05.011.
14. S. Gunasundari, M.S. Ananthi, Comparison and evaluation of methods for liver tumor classification from CT datasets, International Journal of Computer Applications, 39(18): 46–51, 2012, doi: 10.5120/5083-7333.
15. S. Gunasundari, S. Janakiraman, A study of textural analysis methods for the diagnosis of liver diseases from abdominal computed tomography, International Journal of Computer Applications, 74(11): 7–12, 2013, doi: 10.5120/12927-9800.
16. S. Gunasundari, S. Janakiraman, S. Meenambal, Velocity bounded Boolean particle swarm optimization for improved feature selection in liver and kidney disease diagnosis, Expert Systems with Applications, 56: 28–47, 2016, doi: 10.1016/j.eswa.2016.02.042.
17. S. Gunasundari, S. Janakiraman, S. Meenambal, Multiswarm heterogeneous binary PSO using win-win approach for improved feature selection in liver and kidney disease diagnosis, Computerized Medical Imaging and Graphics, 70: 135–154, 2018, doi: 10.1016/j.compmedimag.2018.10.003.
18. S. Gunasundari, S. Janakiraman, S. Meenambal, Embedded binary PSO integrating classical methods for multilevel improved feature selection in liver disease diagnosis, International Journal of Biomedical Engineering and Technology, 31(2): 105–136, 2019, doi: 10.1504/IJBET.2019.102119.
19. Y.L. Huang, J.H. Chen, W.C. Shen, Diagnosis of hepatic tumors with texture analysis in no enhanced computed tomography images, Academic Radiology, 13(6): 713–720, 2006, doi: 10.1016/j.acra.2005.07.014.
20. T. Kondo, J. Ueno, S. Takao, Hybrid GMDH type neural network using artificial intelligence and its application to medical image diagnosis of liver cancer, [in:] 2011 IEEE/SICE International Symposium on System Integration, pp. 1101–1106, 2011, doi: 10.1109/SII.2011.6147603.
21. S.S. Kumar, R.S. Moni, Diagnosis of liver tumor from CT images using fast discrete curvelet transform, IJCA Special Issue on Computer Aided Soft Computing Techniques for Imaging and Biomedical Applications CASCT, 2(4): 1173–1178, 2010, doi: 10.5120/999-34.
22. S.S. Kumar, R.S. Moni, J. Rajeesh, Liver tumor diagnosis by gray level and contourlet coefficients texture analysis, [in:] International Conference on Computing, Electronics and Electrical Technologies, pp. 557–562, 2012, doi: 10.1109/ICCEET.2012.6203881.
23. S.S. Kumar, R.S. Moni, J. Rajeesh, An automatic computer-aided diagnosis system for liver tumors on computed tomography images, Computers and Electrical Engineering, 39: 1516–1526, 2013, doi: 10.1016/j.compeleceng.2013.02.008.
24. W.-J. Kuo, Computer-aided diagnosis for feature selection and classification of liver tumors in computed tomography images, [in:] Proceedings of IEEE International Conference on Applied System Innovation, pp. 1207–1210, 2018, doi: 10.1109/ICASI.2018.8394505.
25. C.C. Lee, S.H. Chen, Y.C. Chia, Classification of liver disease from CT images using a support vector machine, Journal of Advanced Computational Intelligence and Intelligent Informatics, 11(4): 396–402, 2007, doi: 10.20965/jaciii.2007.p0396.
26. C.C. Lee, Y.C. Chiang, C.L. Tsai, S.H. Chen, Distinction of liver disease from CT images using kernel-based classifiers, International Journal of Intelligent Computing in Medical Sciences & Image Processing, 1(2): 113–120, 2007, doi: 10.1080/1931308X.2007.10644144.
27. S. Lee et al., Liver imaging features by convolutional neural network to predict the metachronous liver metastasis in stage I-III colorectal cancer patients based on preoperative abdominal CT scan, [in:] The 18th Asia Pacific Bioinformatics Conference, Seoul, Korea, pp. 1–14, 2020, doi: 10.1186/s12859-020-03686-0.
28. J. Li, J. Sun, N. Shen, E. Chen, Y. Zhang, A CAD system for liver cancer diagnosis based on multi-phase CT images features with BP network, [in:] 11th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), pp. 67–70, 2019, doi: 10.1109/IHMSC.2019.10111.
29. J. Li et al., A fully automatic computer-aided diagnosis system for hepatocellular carcinoma using convolutional neural networks, Biocybernetics and Biomedical Engineering, 40(1): 238–248, 2020, doi: 10.1016/j.bbe.2019.05.008.
30. K. Mala, V. Sadasivam, Automatic segmentation and classification of diffused liver diseases using wavelet based texture analysis and neural network, [in:] IEEE Indicon 2005 Conference, Chennai, India, pp. 216–219, 2005, doi: 10.1109/INDCON.2005.1590158.
31. A.S. Ladkat, S.S. Patankar, J.V. Kulkarni, Modified matched filter kernel for classification of hard exudate, [in:] 2016 International Conference on Inventive Computation Technologies (ICICT), pp. 1–6, 2016, doi: 10.1109/INVENTIVE.2016.7830123.
32. A.S. Ladkat et al., Deep neural network-based novel mathematical model for 3D brain tumor segmentation, Computational Intelligence and Neuroscience, 2022: Article ID 4271711, 8 pages, 2022, doi: 10.1155/2022/4271711.
33. M. Shobana et al., Classification and detection of mesothelioma cancer using feature selection-enabled machine learning technique, BioMed Research International, 2022: Article ID 9900668, 6 pages, 2022, doi: 10.1155/2022/9900668.
34. A.S. Ladkat, A.A. Date, S.S. Inamdar, Development and comparison of serial and parallel image processing algorithms, [in:] 2016 International Conference on Inventive Computation Technologies (ICICT), pp. 1–4, 2016, doi: 10.1109/INVENTIVE.2016.7824894.
Published
Mar 1, 2023
How to Cite
DESHMUKH, Sunita P. et al. Hybrid Deep Learning Method for Detection of Liver Cancer. Computer Assisted Methods in Engineering and Science, [S.l.], v. 30, n. 2, p. 151–165, mar. 2023. ISSN 2956-5839. Available at: <https://cames.ippt.pan.pl/index.php/cames/article/view/463>. Date accessed: 15 apr. 2024. doi: http://dx.doi.org/10.24423/cames.463.
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Articles