Detection of Liver Cancer from CT Scan Images Using Convolutional Neural Network and Support Vector Machine

Davrpanah, Pegah; Saraf Esmaili, Somayeh

doi:10.30473/jphys.2024.71893.1204

Document Type : Research

Authors

¹ Ph.D. Student in Biomedical Engineering, Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

² Assistant Professor, Department of Biomedical Engineering, Garmsar Branch, Islamic Azad University, Garmsar, Iran.

https://doi.org/10.30473/jphys.2024.71893.1204

Abstract

Liver cancer is the fifth most common cancer in men and the seventh most common cancer in women, and is the third leading cause of cancer-related deaths worldwide. In general, the disease is less common in women, and in most regions of the world, the rate of liver cancer in men is two to three times higher than in women, which is probably due to the higher prevalence of risk factors in men and gender differences. In this regard, the aim of this research was to detect liver cancer from CT scan images using a convolutional neural network and support vector machine. In this research, a new lightweight CNN neural network with seven layers and only one conventional layer is proposed for segmented liver classification. This proposed model used in two different ways. The first path used deep learning classification and achieved accuracies of 83.7% and 95.9%. Meanwhile, the second route used automatically extracted features together with a support vector machine (SVM) classifier and achieved accuracies of 95.9% and 97.9%. The proposed network is lightweight, fast, reliable and accurate. This approach can used by an oncologist, making detection a simple task. In addition, the proposed network achieves high accuracy without adjusting images, which reduces time and cost.

Keywords

References

[1] Aghamohammadi, A., Ranjbarzadeh, R., Naiemi, F., Mogharrebi, M., Dorosti, S., & Bendechache, M. (2021). TPCNN: two-path convolutional neural network for tumor and liver segmentation in CT images using a novel encoding approach. Expert Systems with Applications, 183, 115406.

[2] Al-Saeed, Y., Gab-Allah, W. A., Soliman, H., Abulkhair, M. F., Shalash, W. M., & Elmogy, M. (2022). Efficient computer aided diagnosis system for hepatic tumors using computed tomography scans. CMC-Comput Mater Cont, 71 (3), 4871-94.

[3] Araújo, J. D. L., da Cruz, L. B., Ferreira, J. L., da Silva Neto, O. P., Silva, A. C., de Paiva, A. C., & Gattass, M. (2021). An automatic method for segmentation of liver lesions in computed tomography images using deep neural networks. Expert Systems with Applications, 180, 115064.

[4] Ayalew, Y. A., Fante, K. A., & Mohammed, M. A. (2021). Modified U-Net for liver cancer segmentation from computed tomography images with a new class balancing method. BMC Biomedical Engineering, 3, 1-13.

[5] Azer, S. A. (2019). Deep learning with convolutional neural networks for identification of liver masses and hepatocellular carcinoma: A systematic review. World journal of gastrointestinal oncology, 11 (12), 1218.

[6] Bruix, J., Han, K. H., Gores, G., Llovet, J. M., & Mazzaferro, V. (2015). Liver cancer: approaching a personalized care. Journal of hepatology, 62 (1), S144-S156.

[7] Cheng, L., Zhang, Z., Zuo, D., Zhu, W., Zhang, J., Zeng, Q., ... & Zhao, Y. (2018). Ultrasensitive detection of serum microRNA using branched DNA-based SERS platform combining simultaneous detection of α-fetoprotein for early diagnosis of liver cancer. ACS applied materials & interfaces, 10 (41), 34869-34877.

[8] Chlebus, G., Schenk, A., Moltz, J. H., van Ginneken, B., Hahn, H. K., & Meine, H. (2018). Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing. Scientific reports, 8 (1), 15497.

[9] Dong, X., Zhou, Y., Wang, L., Peng, J., Lou, Y., & Fan, Y. (2020). Liver cancer detection using hybridized fully convolutional neural network based on deep learning framework. IEEE Access, 8, 129889-129898.

[10] Frid-Adar, M., Klang, E., Amitai, M., Goldberger, J., & Greenspan, H. (2018, April). Synthetic data augmentation using GAN for improved liver lesion classification. In 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018) (pp. 289-293). IEEE.

[11] Frid-Adar, M., Klang, E., Amitai, M., Goldberger, J., & Greenspan, H. (2018, April). Synthetic data augmentation using GAN for improved liver lesion classification. In 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018) (pp. 289-293). IEEE.

[12] Ghoniem, R. M. (2020). A novel bio-inspired deep learning approach for liver cancer diagnosis. Information, 11 (2), 80.

[13] Hamm, C. A., Wang, C. J., Savic, L. J., Ferrante, M., Schobert, I., Schlachter, T., ... & Letzen, B. (2019). Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI. European radiology, 29, 3338-3347.

[14] Kakkar, P., Nagpal, S., & Nanda, N. (2018). Automatic liver segmentation in CT images using improvised techniques. In Smart Health: International Conference, ICSH 2018, Wuhan, China, July 1–3, 2018, Proceedings 6 (pp. 41-52). Springer International Publishing.

[15] Kavur, A. E., Kuncheva, L. I., & Selver, M. A. (2020). Basic ensembles of vanilla-style deep learning models improve liver segmentation from ct images. In Convolutional Neural Networks for Medical Image Processing Applications (pp. 52-74). CRC Press.

[16] Li, C., Tan, Y., Chen, W., Luo, X., Gao, Y., Jia, X., & Wang, Z. (2020, October). Attention unet++: A nested attention-aware u-net for liver ct image segmentation. In 2020 IEEE international conference on image processing (ICIP) (pp. 345-349). IEEE.

[17] Li, J., Wu, Y., Shen, N., Zhang, J., Chen, E., Sun, J., ... & Zhang, Y. (2020). A fully automatic computer-aided diagnosis system for hepatocellular carcinoma using convolutional neural networks. Biocybernetics and Biomedical Engineering, 40 (1), 238-248.

[18] Liu, S., Wang, M., Zheng, C., Zhong, Q., Shi, Y., & Han, X. (2020). Diagnostic value of serum glypican-3 alone and in combination with AFP as an aid in the diagnosis of liver cancer. Clinical biochemistry, 79, 54-60.

[19] Liu, Z., Suo, C., Mao, X., Jiang, Y., Jin, L., Zhang, T., & Chen, X. (2020). Global incidence trends in primary liver cancer by age at diagnosis, sex, region, and etiology, 1990‐2017. Cancer, 126 (10), 2267-2278.

[20] Meng, L., Zhang, Q., & Bu, S. (2021). Two-stage liver and tumor segmentation algorithm based on convolutional neural network. Diagnostics, 11 (10), 1806.

[21] Meng, L., Zhang, Q., & Bu, S. (2021). Two-stage liver and tumor segmentation algorithm based on convolutional neural network. Diagnostics, 11 (10), 1806.

[22] Naaqvi, Z., Akbar, S., Hassan, S. A., & Ain, Q. U. (2022, May). Detection of Liver Cancer through Computed Tomography Images using Deep Convolutional Neural Networks. In 2022 2nd International Conference on Digital Futures and Transformative Technologies (ICoDT2) (pp. 1-6). IEEE.

[23] Nakai, H., Fujimoto, K., Yamashita, R., Sato, T., Someya, Y., Taura, K., ... & Nakamoto, Y. (2021). Convolutional neural network for classifying primary liver cancer based on triple-phase CT and tumor marker information: a pilot study. Japanese Journal of Radiology, 39, 690-702.

[24] Nisa, M., Buzdar, S. A., Khan, K., & Ahmad, M. S. (2022). Deep Convolutional Neural Network Based Analysis of Liver Tissues Using Computed Tomography Images. Symmetry, 14 (2), 383.

[25] Peng, J., Kang, S., Ning, Z., Deng, H., Shen, J., Xu, Y., ... & Liu, L. (2020). Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging. European radiology, 30, 413-424.

[26] Rela, M., Suryakari, N. R., & Patil, R. R. (2022). A diagnosis system by U-net and deep neural network enabled with optimal feature selection for liver tumor detection using CT images. Multimedia Tools and Applications, 1-43.

[27] Sumathy, B., Dadheech, P., Jain, M., Saxena, A., Hemalatha, S., Liu, W., & Nuagah, S. J. (2022). A Liver Damage Prediction Using Partial Differential Segmentation with Improved Convolutional Neural Network. Journal of Healthcare Engineering, 2022.

[28] Trivizakis, E., Manikis, G. C., Nikiforaki, K., Drevelegas, K., Constantinides, M., Drevelegas, A., & Marias, K. (2018). Extending 2-D convolutional neural networks to 3-D for advancing deep learning cancer classification with application to MRI liver tumor differentiation. IEEE journal of biomedical and health informatics, 23 (3), 923-930.

[29] Wang, C. J., Hamm, C. A., Savic, L. J., Ferrante, M., Schobert, I., Schlachter, T., ... & Letzen, B. (2019). Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features. European radiology, 29, 3348-3357.

Quarterly Journal of Optoelectronic

Detection of Liver Cancer from CT Scan Images Using Convolutional Neural Network and Support Vector Machine

References

References

Volume 7, Issue 1 - Serial Number 18
November 2024
Pages 35-46

Detection of Liver Cancer from CT Scan Images Using Convolutional Neural Network and Support Vector Machine

References

References

Volume 7, Issue 1 - Serial Number 18November 2024Pages 35-46

Volume 7, Issue 1 - Serial Number 18
November 2024
Pages 35-46