Evaluasi Model Deep Learning pada Pola Dataset Biomedis

Authors

  • Gunawan Gunawan STMIK YMI Tegal
  • Septian Ari Wibowo STMIK YMI Tegal
  • Wresti Andriani STMIK YMI Tegal

DOI:

https://doi.org/10.33020/saintekom.v14i2.738

Keywords:

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.

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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

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Published

30-09-2024

How to Cite

Gunawan, Gunawan, Septian Ari Wibowo, and Wresti Andriani. 2024. “Evaluasi Model Deep Learning Pada Pola Dataset Biomedis”. Jurnal Saintekom : Sains, Teknologi, Komputer Dan Manajemen 14 (2):195-207. https://doi.org/10.33020/saintekom.v14i2.738.