Evaluasi Model Deep Learning pada Pola Dataset Biomedis
DOI:
https://doi.org/10.33020/saintekom.v14i2.738Keywords:
Biomedical Data, CNN (Convolutional Neural Networks), Deep Learning, Medical Image Analysis, RNN (Recurrent Neural Networks)Abstract
This study aims to evaluate the effectiveness and efficiency of various deep learning models in recognizing patterns within diverse biomedical datasets. The methods involved the collection of biomedical data from various public and synthetic sources, including chest radiographs, MRI, CT scans, as well as electrocardiogram (ECG) and electromyography (EMG) signals. The data underwent preprocessing steps such as normalization, noise removal, and data augmentation to improve quality and variability. The deep learning models evaluated included Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), which were trained to identify patterns within the data. The performance evaluation was conducted using metrics like accuracy, sensitivity, specificity, and AUC to ensure the models' generalization capabilities on test datasets. The results revealed that CNNs excelled in medical image analysis, particularly in terms of accuracy and interpretability, while RNNs were more effective in handling sequential data such as medical signals. The primary conclusion of this study is that the selection of deep learning models should be tailored to the type of data and specific application requirements, emphasizing the importance of improving model interpretability and generalization for broader applications in clinical settings.
Downloads
References
Ali, A. H., Mohanad G. Yaseen, Mohammad Aljanabi, & Saad Abbas Abed. (2023). Transfer Learning: A New Promising Techniques . Mesopotamian Journal of Big Data, 2023(SE-Articles), 29–30. https://doi.org/10.58496/MJBD/2023/004
Ben Yedder, H., Cardoen, B., & Hamarneh, G. (2021). Deep learning for biomedical image reconstruction: A survey. Artificial Intelligence Review, 54(1), 215–251. https://doi.org/10.1007/s10462-020-09861-2
Carrington, A. M., Manuel, D. G., Fieguth, P. W., Ramsay, T., Osmani, V., Wernly, B., Bennett, C., Hawken, S., Magwood, O., & Sheikh, Y. (2022). Deep ROC analysis and AUC as balanced average accuracy, for improved classifier selection, audit and explanation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(1), 329–341. https://doi.org/10.1109/TPAMI.2022.3145392
Chen, L., Li, S., Bai, Q., Yang, J., Jiang, S., & Miao, Y. (2021). Review of image classification algorithms based on convolutional neural networks. Remote Sensing, 13(22), 4712. https://doi.org/10.3390/rs13224712
Chen, Z., Duan, J., Kang, L., & Qiu, G. (2021). A hybrid data-level ensemble to enable learning from highly imbalanced dataset. Information Sciences, 554, 157–176. https://doi.org/10.1016/j.ins.2020.12.023
Chicco, D., & Jurman, G. (2023). The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification. BioData Mining, 16(1), 4. https://doi.org/10.1186/s13040-023-00322-4
Davamani, K. A., Robin, C. R. R., Amudha, S., & Anbarasi, L. J. (2021). Biomedical image segmentation by deep learning methods. Computational Analysis and Deep Learning for Medical Care: Principles, Methods, and Applications, 131–154. https://doi.org/10.1002/9781119785750.ch6
Liang, Y., Li, S., Yan, C., Li, M., & Jiang, C. (2021). Explaining the black-box model: A survey of local interpretation methods for deep neural networks. Neurocomputing, 419, 168–182. https://doi.org/10.1016/j.neucom.2020.08.011
Mahmud, M., Kaiser, M. S., McGinnity, T. M., & Hussain, A. (2021). Deep learning in mining biological data. Cognitive Computation, 13(1), 1–33. https://doi.org/10.1007/s12559-020-09773-x
Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018). Deep learning for healthcare: review, opportunities and challenges. Briefings in Bioinformatics, 19(6), 1236–1246.
Moskolaï, W. R., Abdou, W., Dipanda, A., & Kolyang. (2021). Application of deep learning architectures for satellite image time series prediction: A review. Remote Sensing, 13(23), 4822. https://doi.org/10.3390/rs13234822
Mouliou, D. S., & Gourgoulianis, K. I. (2021). False-positive and false-negative COVID-19 cases: respiratory prevention and management strategies, vaccination, and further perspectives. Expert Review of Respiratory Medicine, 15(8), 993–1002. https://doi.org/10.1080/17476348.2021.1917389
Narkhede, M. V, Bartakke, P. P., & Sutaone, M. S. (2022). A review on weight initialization strategies for neural networks. Artificial Intelligence Review, 55(1), 291–322. https://doi.org/10.1007/s10462-021-10033-z
Shvetsova, N., Bakker, B., Fedulova, I., Schulz, H., & Dylov, D. V. (2021). Anomaly detection in medical imaging with deep perceptual autoencoders. IEEE Access, 9, 118571–118583. https://doi.org/10.1109/ACCESS.2021.3107163
Stahlschmidt, S. R., Ulfenborg, B., & Synnergren, J. (2022). Multimodal deep learning for biomedical data fusion: a review. Briefings in Bioinformatics, 23(2), bbab569. https://doi.org/10.1093/bib/bbab569
Suganyadevi, S., Seethalakshmi, V., & Balasamy, K. (2022). A review on deep learning in medical image analysis. International Journal of Multimedia Information Retrieval, 11(1), 19–38. https://doi.org/10.1007/s13735-021-00218-1
Talaei Khoei, T., Ould Slimane, H., & Kaabouch, N. (2023). Deep learning: systematic review, models, challenges, and research directions. Neural Computing and Applications, 35(31), 23103–23124. https://doi.org/10.1007/s00521-023-08957-4
Wang, X., & Cheng, Z. (2020). Cross-Sectional Studies: Strengths, Weaknesses, and Recommendations. Chest, 158(1, Supplement), S65–S71. https://doi.org/https://doi.org/10.1016/j.chest.2020.03.012
Woessner, A. E., Anjum, U., Salman, H., Lear, J., Turner, J. T., Campbell, R., Beaudry, L., Zhan, J., Cornett, L. E., Gauch, S., & Quinn, K. P. (2024). Identifying and training deep learning neural networks on biomedical-related datasets. Briefings in Bioinformatics, 25(Supplement_1), bbae232. https://doi.org/10.1093/bib/bbae232
Xiao, S., Wu, Y., & Liu, H. (2020). Evolving status of the 2019 novel coronavirus infection: Proposal of conventional serologic assays for disease diagnosis and infection monitoring. Journal of Medical Virology, 92(5), 464. https://doi.org/10.1002/jmv.25702
Zhang, Y., Dong, Z., Li, S., & Cattani, C. (2023). Deep learning methods for biomedical information analysis. Journal of Ambient Intelligence and Humanized Computing, 14(5), 5293–5296. https://doi.org/10.1007/s12652-023-04617-6
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Gunawan Gunawan, Septian Ari Wibowo, Wresti Andriani
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Copyright :
By submitting manuscripts to Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen, the author agrees with this policy. No specific document approval is required.
- The copyright in each article belongs to the author.
- Authors retain all their rights to the published work, not limited to the rights set forth in this page.
- Authors acknowledge that Saintekom Journal: Science, Technology, Computers and Management as the first to publish under the Creative Commons Attribution 4.0 International license (CC BY-SA).
- The author may submit the paper separately, arrange for non-exclusive distribution of the manuscript that has been published in this journal into other versions (e.g. sent to the author's institutional respository, publication into a book, etc.), by acknowledging that the manuscript has been first published Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen;
- The author warrants that the article is original, written by the named author, has not been previously published, contains no unlawful statements, does not infringe the rights of others, is subject to copyright exclusively held by the author.
- If the article is jointly prepared by more than one author, each author submitting the manuscript warrants that he or she has been authorized by all co-authors to agree to copyright and license notices (agreements) on their behalf, and agrees to inform co-authors of the terms of this policy. Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen will not be held liable for anything that may arise due to internal author disputes.
Lisensi :
Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen is published under the terms of the Creative Commons Attribution 4.0 International License (CC BY-SA). This license permits anyone to:.
- Share - copy and redistribute this material in any form or format;
- Adaptation - modify, alter, and create derivatives of this material for any purpose.
- Attribution - you must give appropriate credit, include a link to the license, and state that changes have been made. You may do this in any appropriate manner, but it does not imply that the licensor endorses you or your use.
- Similar Sharing - If you modify, alter, or create a derivative of this material, you must distribute your contribution under the same license as the original material.
Most read articles by the same author(s)
- Gunawan Gunawan, Muhammad Rizki Zulkarnain, ANALISIS PENERIMAAN PENGGUNA E-RAPOR SMP MENGGUNAKAN EXTENDED TECHNOLOGY ACCEPTANCE MODEL , Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen: Vol. 11 No. 2 (2021): September 2021