CNN with machine learning approaches using ExtraTreesClassifier and MRMR feature selection techniques to detect liver diseases on cloud
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Software
Link
https://link.springer.com/content/pdf/10.1007/s10586-022-03752-7.pdf
Reference63 articles.
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2. Shahabi, M., Hassanpour, H., Mashayekhi, H.: Rule extraction for fatty liver detection using neural networks. Neural Comput. & Applic. 31, 979–989 (2019). https://doi.org/10.1007/s00521-017-3130-5
3. Ali, L., Wajahat, I., Amiri Golilarz, N., Keshtkar, F., Bukhari, S.A.C.: LDA–GA–SVM: improved hepatocellular carcinoma prediction through dimensionality reduction and genetically optimized support vector machine. Neural Comput. & Applic. 33, 2783–2792 (2021). https://doi.org/10.1007/s00521-020-05157-2
4. Grissa, D., Nytoft Rasmussen, D., Krag, A., Brunak, S., Juhl Jensen, L.: Alcoholic liver disease: A registry view on comorbidities and disease prediction. PLoS Comput. Biol. 16, e1008244 (2020). https://doi.org/10.1371/journal.pcbi.1008244
5. Hashem, S., ElHefnawi, M., Habashy, S., El-Adawy, M., Esmat, G., Elakel, W., Abdelazziz, A.O., Nabeel, M.M., Abdelmaksoud, A.H., Elbaz, T.M., Shousha, H.I.: Machine Learning Prediction Models for Diagnosing Hepatocellular Carcinoma with HCV-related Chronic Liver Disease. Comput. Methods Programs Biomed. 196, 105551 (2020). https://doi.org/10.1016/j.cmpb.2020.105551
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