Detection of Liver Cirrhosis using a Web-Based Convolutional Neural Network System
DOI:
https://doi.org/10.32628/CSEIT25113395Keywords:
Liver Cirrhosis, Convolutional Neural Network, Deep Learning, Medical Image Classification, Web-Based System, Early DiagnosisAbstract
Liver cirrhosis, a critical health condition marked by irreversible scarring of the liver, contributes significantly to global morbidity and mortality. Traditional diagnostic methods are invasive, costly, and often detect the disease at advanced stages. This study presents the design and implementation of a non-invasive, web-based liver cirrhosis detection system employing Convolutional Neural Networks (CNNs). The system aims to support early diagnosis and prognosis using medical imaging and machine learning. The implemented system demonstrates high accuracy in classifying cirrhotic conditions and offers a scalable solution for under-resourced medical facilities
📊 Article Downloads
References
Al-Hasani, M., Sultan, L. R., Sagreiya, H., Cary, T. W., Karmacharya, M. B., & Sehgal, C. M. (2022). Machine learning improves early detection of liver fibrosis by quantitative ultrasound radiomics. IEEE International Ultrasonics Symposium, 2022 DOI: https://doi.org/10.1109/IUS54386.2022.9957180
Amin, R., Yasmin, R., Ruhi, S., Rahman, M. H., & Reza, M. S. (2023). Prediction of chronic liver disease patients using integrated projection based statistical feature extraction with machine learning algorithms. Informatics in Medicine Unlocked, 36, 101155. DOI: https://doi.org/10.1016/j.imu.2022.101155
Bhute, P., Gahane, S., Kapse, A., Mohadure, S., Raut, E., & Khode, P. (2024, December). Cloud Cryptography in IT Infrastructure: A Comprehensive Review and Future Directions. In 2024 International Conference on Sustainable Communication Networks and Application (ICSCNA) (pp. 528-533). IEEE. DOI: https://doi.org/10.1109/ICSCNA63714.2024.10863893
Blesl, A., & Stadlbauer, V. (2021). The gut-liver axis in cholestatic liver diseases. In Nutrients (Vol. 13, Issue 3, pp. 1–32). https://doi.org/10.3390/nu13031018 DOI: https://doi.org/10.3390/nu13031018
Devarbhavi, H., Asrani, S. K., Arab, J. P., Nartey, Y. A., Pose, E., & Kamath, P. S. (2023). Global burden of liver disease: 2023 update. Journal of Hepatology, 79(2), 516–537. https://doi.org/10.1016/j.jhep.2023.03.017 DOI: https://doi.org/10.1016/j.jhep.2023.03.017
Ehman, R. L. (2022). Magnetic resonance elastography: from invention to standard of care. Abdominal Radiology, 47(9), 3028–3036. https://doi.org/10.1007/s00261-022-03597-z DOI: https://doi.org/10.1007/s00261-022-03597-z
Endah, M., Wijaya, R. N., Ahsan, H. K., & Endah Hiswati, M. (2023). Enhancing cirrhosis detection: A deep learning approach with convolutional neural networks. Journal of Soft Computing Exploration. https://doi.org/10.52465/joscex.v4i4.226 DOI: https://doi.org/10.52465/joscex.v4i4.226
Hamid, K., Asif, A., Abbasi, W., & Sabih, D. (2017). Machine learning with abstention for automated liver disease diagnosis. 2017 International Conference on Frontiers of Information Technology (FIT), 356–361. DOI: https://doi.org/10.1109/FIT.2017.00070
Ignat, S.-R., Dinescu, S., Hermenean, A., & Costache, M. (2020). Cellular interplay as a consequence of inflammatory signals leading to liver fibrosis development. Cells, 9(2), 461. DOI: https://doi.org/10.3390/cells9020461
Kaiser, S., Sziranyi, J., & Groß, D. (2020). The hepatopathologist Hans Popper (1903–1988). Der Pathologe, 41(1), 30–38. https://doi.org/10.1007/s00292-019-0619-y DOI: https://doi.org/10.1007/s00292-019-0619-y
Liu, Z.-P., Ouyang, G.-Q., Huang, G.-Z., Wei, J., Dai, L., He, S.-Q., & Yuan, G.-D. (2023). Global burden of cirrhosis and other chronic liver diseases due to nonalcoholic fatty liver disease, 1990-2019. World Journal of Hepatology, 15(11), 1210–1225. https://doi.org/10.4254/wjh.v15.i11.1210 DOI: https://doi.org/10.4254/wjh.v15.i11.1210
Mazen, T. Sh. (2023). Machine Learning-based Segmentation to the Prediction of Liver Cirrhosis. International Journal of Computer Applications, 185(23), 13–20. https://doi.org/10.5120/ijca2023922973 DOI: https://doi.org/10.5120/ijca2023922973
Meza, J. K. S., Yepes, D. O., Rodrigo-Ilarri, J., & Cassiraga, E. (2019). Predictive analysis of urban waste generation for the city of Bogotá, Colombia, through the implementation of decision trees-based machine learning, support vector machines and artificial neural networks. Heliyon, 5(11). DOI: https://doi.org/10.1016/j.heliyon.2019.e02810
Mousa, O. Y., & Kamath, P. S. (2021a). A History of the Assessment of Liver Performance. In Clinical Liver Disease (Vol. 18, Issue S1, pp. 28–48). https://doi.org/10.1002/cld.1100 DOI: https://doi.org/10.1002/cld.1100
Mousa, O. Y., & Kamath, P. S. (2021b). An Official Learning Resource of AASLD review a History of the assessment of liver Performance. 28 | CliniCal Liver Disease, 18, 1. https://doi.org/10.1002/cld.1100/suppinfo DOI: https://doi.org/10.1002/cld.1100
Ndow, Gibril, Erwan Vo-Quang, Yusuke Shimakawa, Amie Ceesay, Saydiba Tamba, Harr F. Njai, Lamin Bojang et al. "Clinical characteristics and outcomes of patients with cirrhosis and hepatocellular carcinoma in The Gambia, west Africa: a prospective cohort study." The Lancet Global Health 11, no. 9 (2023): e1383-e1392. DOI: https://doi.org/10.1016/S2214-109X(23)00263-2
Prakash, B., Vennela, D., Lakshmi, D., & Priyanka, S. S. (2024). Stage Prediction of Liver Cirrhosis Disease using Machine Learning. 1–6. https://doi.org/10.1109/icecsp61809.2024.10698096 DOI: https://doi.org/10.1109/ICECSP61809.2024.10698096
Raschka, S., Patterson, J., & Nolet, C. (2020). Machine learning in python: Main developments and technology trends in data science, machine learning, and artificial intelligence. Information, 11(4), 193. DOI: https://doi.org/10.3390/info11040193
Singh, A., & Kaur, P. J. (2019). Analysis of Software Development Life Cycle Models BT - Proceeding of the Second International Conference on Microelectronics, Computing & Communication Systems (MCCS 2017) (V. Nath & J. K. Mandal (Eds.); pp. 689–699). Springer Singapore DOI: https://doi.org/10.1007/978-981-10-8234-4_55
Smith, D. R. (2022). Mechanical evaluation of structure and function in fibrous soft tissue using MR elastography. University of Delaware.
Wieczorek, M., Weston, A., Ledenko, M., Thomas, J. N., Carter, R., & Patel, T. (2022). A deep learning approach for detecting liver cirrhosis from volatolomic analysis of exhaled breath. Frontiers in Medicine, 9, 992703. DOI: https://doi.org/10.3389/fmed.2022.992703
Wong, A., Anantrasirichai, N., Chalidabhongse, T. H., Palasuwan, D., Palasuwan, A., & Bull, D. (2021). Analysis of vision-based abnormal red blood cell classification. ArXiv Preprint ArXiv:2106.00389.
Yoo, J. J., Namdar, K., McIntosh, C., Khalvati, F., & Rogalla, P. (2022). A Comprehensive Study of Radiomics-based Machine Learning for Fibrosis Detection. ArXiv Preprint ArXiv:2211.14396.
Yoo, J. J., Namdar, K., McIntosh, C., Khalvati, F., & Rogalla, P. (2022). A Comprehensive Study of Radiomics-based Machine Learning for Fibrosis Detection. ArXiv Preprint ArXiv:2211.14396.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
