Deep Learning Based Cirrhosis Detection

Keywords: cirrhosis detection, deep learning, machine learning, MLP

Abstract

Cirrhosis is a liver disease caused by long-term liver damage. Scar tissue caused by cirrhosis prevents the liver from working properly. With the hepatitis C virus, 130-150 million people are infected in the world and 350-500 thousand deaths, and 3-4 million new cases are reported every year due to liver disease. In 2030, it is predicted that there will be 40 percent increase in compensated cirrhosis due to the hepatitis C virus, 60 percent increase in decompensated cirrhosis, and 70 percent increase in liver-related deaths. Although it is difficult to diagnose cirrhosis in the early stages, it is very important step for its treatment. Blood tests, imaging tests, and biopsy methods are used to detect cirrhosis. Due to the costs of these tests and the inability to get the test results immediately, the treatment of the patients cannot be started immediately. In this study, a MLP-based deep learning model has been developed for the prediction of cirrhosis. The developed model has been compared with DT, kNN, LR, NB, RF, and SVM. Experimental studies using the accuracy, precision, recall, and F1-score showed that the developed model was more successful than the compared models. Experimental results showed that the developed model had 80.48% accuracy, 85.71% precision, 85.71% recall, and 85.71% F1-score. Experimental results showed that the developed model had a prediction accuracy of over 80% and F1-score of over 85% in cirrhosis detection from blood tests. The developed model can be used in real-world applications to alleviate the workload of healthcare professionals and to develop early diagnosis systems.

Downloads

Download data is not yet available.

References

Acharya U. R., Faust O., Molinari F., Sree S. V., Junnarkar S. P., Sudarshan V. (2015). Ultrasound-based tissue characterization and classification of fatty liver disease: A screening and diagnostic paradigm. Knowledge-Based Systems, 75, 66-77. https://doi.org/10.1016/j.knosys.2014.11.021 DOI: https://doi.org/10.1016/j.knosys.2014.11.021

Aksu D., Üstebay S., Aydin M. A., Atmaca T. (2018). Intrusion detection with comparative analysis of supervised learning techniques and fisher score feature selection algorithm. In International symposium on computer and information sciences, 141-149. https://doi.org/10.1007/978-3-030-00840-6_16

Allugunti V. R. (2022). Breast cancer detection based on thermographic images using machine learning and deep learning algorithms. International Journal of Engineering in Computer Science, 4(1), 49-56.

Almadhoun H. R., Abu-Naser S. S. (2022). Detection of Brain Tumor Using Deep Learning. International Journal of Academic Engineering Research (IJAER), 6(3).

Amitrano L., Guardascione M. A., Brancaccio V., Balzano A. (2002). Coagulation disorders in liver disease. In Seminars in liver disease, 22 (1), 83-96. https://doi.org/10.1055/s-2002-23205 DOI: https://doi.org/10.1055/s-2002-23205

Arroyo V., Moreau R., Kamath P. S., Jalan R., Ginès P., Nevens F., Schnabl B. (2016). Acute-on-chronic liver failure in cirrhosis. Nature reviews Disease primers, 2(1), 1-18. https://doi.org/10.3390/jcm10194406 DOI: https://doi.org/10.1038/nrdp.2016.41

Che Azemin M. Z., Hassan R., Mohd Tamrin M. I., Md Ali M. A. (2020). COVID-19 deep learning prediction model using publicly available radiologist-adjudicated chest X-ray images as training data: preliminary findings. International Journal of Biomedical Imaging. https://doi.org/10.1155/2020/8828855

Garcia‐Martinez R., Caraceni P., Bernardi M., Gines P., Arroyo V., Jalan R. (2013). Albumin: pathophysiologic basis of its role in the treatment of cirrhosis and its complications. Hepatology, 58(5), 1836-1846. https://doi.org/10.1002/hep.26338 DOI: https://doi.org/10.1002/hep.26338

Ginès P., Krag A., Abraldes J. G., Solà E., Fabrellas N., Kamath P. S. (2021). Liver cirrhosis. The Lancet, 398(10308), 1359-1376. https://doi.org/10.1016/S0140-6736(21)01374-X

Goceri E. (2019). Skin disease diagnosis from photographs using deep learning. In ECCOMAS thematic conference on computational vision and medical image processing, 239-246. https://doi.org/10.1007/978-3-030-32040-9_25

Guerci P., Ergin B., Uz Z., Ince Y., Westphal M., Heger M., Ince C. (2019). Glycocalyx degradation is independent of vascular barrier permeability increase in nontraumatic hemorrhagic shock in rats. Anesthesia & Analgesia, 129(2), 598-607. https://doi.org/10.1213/ane.0000000000003918

Ismael A. M., Şengür A. (2021). Deep learning approaches for COVID-19 detection based on chest X-ray images. Expert Systems with Applications, 164, 114054. https://doi.org/10.1016/j.eswa.2020.114054

Jadhav S. D., Channe H. P. (2016). Comparative study of K-NN, naive Bayes and decision tree classification techniques. International Journal of Science and Research (IJSR), 5(1), 1842-1845. https://doi.org/10.21275/v5i1.nov153131 DOI: https://doi.org/10.21275/v5i1.NOV153131

Jain R., Gupta M., Taneja S., Hemanth D. J. (2021). Deep learning based detection and analysis of COVID-19 on chest X-ray images. Applied Intelligence, 51(3), 1690-1700.

Luetkens J. A., Nowak S., Mesropyan N., Block W., Praktiknjo M., Chang J., Attenberger U. (2022). Deep learning supports the differentiation of alcoholic and other-than-alcoholic cirrhosis based on MRI. Scientific reports, 12(1), 1-8. https://doi.org/10.1038/s41598-022-12410-2

Mostafa F., Hasan E., Williamson M., Khan H. (2021). Statistical Machine Learning Approaches to Liver Disease Prediction. Livers, 1(4), 294-312. https://doi.org/10.3390/livers1040023

Mozos I. (2015). Arrhythmia risk in liver cirrhosis. World journal of hepatology, 7(4), 662. https://doi.org/10.1007/s10489-020-01902-1 DOI: https://doi.org/10.4254/wjh.v7.i4.662

Pasyar P., Mahmoudi T., Kouzehkanan S. Z. M., Ahmadian A., Arabalibeik H., Soltanian N., Radmard A. R. (2021). Hybrid classification of diffuse liver diseases in ultrasound images using deep convolutional neural networks. Informatics in Medicine Unlocked, 22. https://doi.org/10.1016/j.imu.2020.100496

Pinto R. B., Schneider A. C. R., da Silveira, T. R. (2015). Cirrhosis in children and adolescents: An overview. World journal of hepatology, 7(3), 392. https://doi.org/10.4254/wjh.v7.i3.392 DOI: https://doi.org/10.4254/wjh.v7.i3.392

Terlapu P. V., Gedela S. B., Gangu V. K., Pemula R. (2022). Intelligent diagnosis system of hepatitis C virus: A probabilistic neural network based approach. International Journal of Imaging Systems and Technology. https://doi.org/10.1002/ima.22746

Van Zutphen T., Ciapaite J., Bloks V. W., Ackereley C., Gerding A., Jurdzinski A., Bandsma R. H. (2016). Malnutrition-associated liver steatosis and ATP depletion is caused by peroxisomal and mitochondrial dysfunction. Journal of hepatology, 65(6), 1198-1208. https://doi.org/10.1016/j.jhep.2016.05.046 DOI: https://doi.org/10.1016/j.jhep.2016.05.046

Vranjkovic A., Deonarine F., Kaka S., Angel J. B., Cooper C. L., Crawley A. M. (2019). Direct-acting antiviral treatment of HCV infection does not resolve the dysfunction of circulating CD8+ T-cells in advanced liver disease. Frontiers in immunology, 10, 1926. https://doi.org/10.3389/fimmu.2019.01926

Younossi Z., Henry L. (2015). Systematic review: patient‐reported outcomes in chronic hepatitis C‐the impact of liver disease and new treatment regimens. Alimentary pharmacology & therapeutics, 41(6), 497-520. https://doi.org/10.1111/apt.13090 DOI: https://doi.org/10.1111/apt.13090

Published
2022-11-17
How to Cite
Utku, A. (2022). Deep Learning Based Cirrhosis Detection. Operational Research in Engineering Sciences: Theory and Applications. https://doi.org/10.31181/oresta171122136u
Section
Articles