Batch size and learning rate effect in covid-19 classification using CNN
Article Sidebar
Published:
Nov 8, 2023
Keywords:
Batch Size, CNN, Covid-19, K-fold validation, Learning Rate
Section
Computer Science
Statistics Article
Article View : 110 Times
Main Article Content
Abstract
This study evaluated the performance of a Convolutional Neural Network (CNN) model in classifying CT-Chest images of COVID-19 and non-COVID patients. The primary focus was to determine the influence of learning rate and batch size on the model's effectiveness. This research used 698 datas from Covid-19 and NonCovid-19 CT-Chest Image. Those dataset was obtained from medRxiv dan bioRxiv and has been approved by radiology expert in Tongji Hospital, China. In this research, COVID-16 dataset was classified by CNN with different batch sizes and learning rates for each iteration. Batch size used in this study were 1, 2, 4, 8, 16, 32, 64, and 128 with learning rates 0,00001; 0,0001; 0,001; 0,01; 0,1 and 1. This study found that this study showed that batch size and learning rate have a positive effect on CNN performance. The lower the learning rate, the lower the batch size will allow the network in the CNN model to perform better in classifying COVID-19 CT-Chest. In Addition, the best batch size for the classification is 64 with learning rate 0,01. These findings provided important insights into how parameters such as learning rate and batch size impacted the performance of the CNN model in classifying COVID-19 CT-Chest
Downloads
Download data is not yet available.
Article Details
How to Cite
Mirayanti, N. K. R. ., Sariyasa, . S. and Gunadi, I. G. A. (2023) “Batch size and learning rate effect in covid-19 classification using CNN”, Jurnal Mantik, 7(3), pp. 1752-1765. doi: 10.35335/mantik.v7i3.4177.
References
Althnian, A., AlSaeed, D., Al-Baity, H., Samha, A., Dris, A. Bin, Alzakari, N., Abou Elwafa, A., & Kurdi, H. (2021). Impact of dataset size on classification performance: An empirical evaluation in the medical domain. Applied Sciences (Switzerland), 11(2), 1–18. https://doi.org/10.3390/app11020796
Alzubaidi, L., Bai, J., Al-Sabaawi, A., Santamaría, J., Albahri, A. ., Al-dabbagh, B., Fadhel, M., Manoufali, M., Zhang, J., Al-Timemy, A., Duan, Y., Abdullah, A., Farhan, L., Lu, Y., Gupta, A., Albu, F., Abbosh, A., & Gu, Y. (2023). A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications. Journal of Big Data, 10. https://doi.org/10.1186/s40537-023-00727-2
Bordelon, B., Pehlevan, C., & Broeck, V. Den. (2022). LEARNING CURVES FOR STOCHASTIC GRADIENT DESCENT ON STRUCTURED FEATURES. 1–21.
Cerqueira, V., Torgo, L., & Mozeti?, I. (2020). Evaluating time series forecasting models: an empirical study on performance estimation methods. Machine Learning, 109(11), 1997–2028. https://doi.org/10.1007/s10994-020-05910-7
Chen, C. H., Lai, J. P., Chang, Y. M., Lai, C. J., & Pai, P. F. (2023). A Study of Optimization in Deep Neural Networks for Regression. Electronics (Switzerland), 12(14), 1–17. https://doi.org/10.3390/electronics12143071
Cronje, J. C. (2020). Designing Questions for Research Design and Design Research in e?Learning. Electronic Journal of E-Learning, 18(1), pp13-24.
Eom, H., & Choi, H. (2021). Alpha-Integration Pooling for Convolutional Neural Networks. Journal of KIISE, 48(7), 774–780. https://doi.org/10.5626/jok.2021.48.7.774
Fryer, L. K., & Dinsmore, D. L. (2020). The Promise and Pitfalls of Self-report: Development, research design and analysis issues, and multiple methods. Frontline Learning Research, 8(3), 1–9.
Goh, E., Yepremyan, A., Wang, J., & Wilson, B. (2023). MAESSTRO?: Masked Autoencoders for Sea Surface Temperature Reconstruction under Occlusion. July, 1–20.
Halmar, H. F. (2020). Pemeriksaan Diagnostik COVID-19. Jurnal Keperawatan Muhammadiyah, 5(1), 222–230.
Hariyani, Y. S., HADIYOSO, S., & SIADARI, T. S. (2020). Deteksi Penyakit Covid-19 Berdasarkan Citra X-Ray Menggunakan Deep Residual Network. ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika, 8(2), 443. https://doi.org/10.26760/elkomika.v8i2.443
Hassan, E., Shams, M. Y., Hikal, N. A., & Elmougy, S. (2023). The effect of choosing optimizer algorithms to improve computer vision tasks: a comparative study. In Multimedia Tools and Applications (Vol. 82, Issue 11). Multimedia Tools and Applications. https://doi.org/10.1007/s11042-022-13820-0
He, X., Xue, F., Ren, X., & You, Y. (2021). Large-Scale Deep Learning Optimizations: A Comprehensive Survey. 1(1). http://arxiv.org/abs/2111.00856
Hensman, P., & Masko, D. (2015). The Impact of Imbalanced Training Data for Convolutional Neural Networks. PhD. https://www.kth.se/social/files/588617ebf2765401cfcc478c/PHensmanDMasko_dkand15.pdf
Hidaka, A., & Kurita, T. (2017). Consecutive Dimensionality Reduction by Canonical Correlation Analysis for Visualization of Convolutional Neural Networks. Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and Its Applications, 2017(0), 160–167. https://doi.org/10.5687/sss.2017.160
Huang, L., Qin, J., Zhou, Y., Zhu, F., Liu, L., & Shao, L. (2023). Normalization Techniques in Training DNNs: Methodology, Analysis and Application. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–20. https://doi.org/10.1109/TPAMI.2023.3250241
Infante, M., Lutman, R. F., Imparato, S., Di Rocco, M., Ceresoli, G. L., Torri, V., Morenghi, E., Minuti, F., Cavuto, S., Bottoni, E., Inzirillo, F., Cariboni, U., Errico, V., Incarbone, M. A., Ferraroli, G., Brambilla, G., Alloisio, M., & Ravasi, G. (2009). Differential diagnosis and management of focal ground-glass opacities. European Respiratory Journal, 33(4), 821–827. https://doi.org/10.1183/09031936.00047908
Jepkoech, J., Mugo, D. M., Kenduiywo, B. K., & Too, E. C. (2021). The Effect of Adaptive Learning Rate on the Accuracy of Neural Networks. International Journal of Advanced Computer Science and Applications, 12(8), 736–751. https://doi.org/10.14569/IJACSA.2021.0120885
Jo, J.-M. (2019). Effectiveness of Normalization Pre-Processing of Big Data to the Machine Learning Performance. The Journal of the Korea Institute of Electronic Communication Sciences, 14, 547–552. https://doi.org/10.13067/JKIECS.2019.14.3.547
Mooijman, P., Catal, C., Tekinerdogan, B., Lommen, A., & Blokland, M. (2023). The effects of data balancing approaches: A case study. Applied Soft Computing, 132, 109853. https://doi.org/10.1016/j.asoc.2022.109853
Parsania, P. S., & Virparia, P. V. (2018). Computational Time Complexity of Image Interpolation Algorithms. International Journal of Computer Sciences and Engineering, 6(7), 491–496. https://doi.org/10.26438/ijcse/v6i7.491496
Pei, X., Zhao, Y. hong, Chen, L., Guo, Q., Duan, Z., Pan, Y., & Hou, H. (2023). Robustness of machine learning to color, size change, normalization, and image enhancement on micrograph datasets with large sample differences. Materials and Design, 232, 112086. https://doi.org/10.1016/j.matdes.2023.112086
Reyad, M., Sarhan, A., & Arafa, M. (2023). A modified Adam algorithm for deep neural network optimization. Neural Computing and Applications, 35, 1–18. https://doi.org/10.1007/s00521-023-08568-z
Sido, J., & Konopik, M. (2019). Deep Learning for Text Data on Mobile Devices. International Conference on Applied Electronics, 2019-Septe, 1–4. https://doi.org/10.23919/AE.2019.8867025
Sugihantono, A., Burhan, E., & Samoedra, E. (2020). Pedoman Pencegahan dan Pengendalian CORONAVIRUS DISEASE (COVID-19) Revisi ke-5. Jakarta: Kementerian Kesehatan Indonesia, 5(2), 1–214.
Susilo, A., Rumende, C. M., Pitoyo, C. W., Santoso, W. D., Yulianti, M., Herikurniawan, H., Sinto, R., Singh, G., Nainggolan, L., Nelwan, E. J., Chen, L. K., Widhani, A., Wijaya, E., Wicaksana, B., Maksum, M., Annisa, F., Jasirwan, C. O. M., & Yunihastuti, E. (2020). Coronavirus Disease 2019: Tinjauan Literatur Terkini. Jurnal Penyakit Dalam Indonesia, 7(1), 45. https://doi.org/10.7454/jpdi.v7i1.415
Swastika, W. (2020). Studi Awal Deteksi COVID-19 Menggunakan Citra CT Berbasis Deep Learning. Jurnal Teknologi Informasi Dan Ilmu Komputer, 7(3), 629. https://doi.org/10.25126/jtiik.2020733399
Yamashita, R., Nishio, M., Gian Do, R. K., & Togash, K. (2018). Convolutional neural networks: an overview and application in radiology. Smart Innovation, Systems and Technologies, 9, 611–629. https://doi.org/10.1007/978-981-15-7078-0_3
Yang, X., He, X., Zhao, J., Zhang, Y., Zhang, S., & Xie, P. (2020). COVID-CT-Dataset: A CT Scan Dataset about COVID-19. 1–14. http://arxiv.org/abs/2003.13865
Alzubaidi, L., Bai, J., Al-Sabaawi, A., Santamaría, J., Albahri, A. ., Al-dabbagh, B., Fadhel, M., Manoufali, M., Zhang, J., Al-Timemy, A., Duan, Y., Abdullah, A., Farhan, L., Lu, Y., Gupta, A., Albu, F., Abbosh, A., & Gu, Y. (2023). A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications. Journal of Big Data, 10. https://doi.org/10.1186/s40537-023-00727-2
Bordelon, B., Pehlevan, C., & Broeck, V. Den. (2022). LEARNING CURVES FOR STOCHASTIC GRADIENT DESCENT ON STRUCTURED FEATURES. 1–21.
Cerqueira, V., Torgo, L., & Mozeti?, I. (2020). Evaluating time series forecasting models: an empirical study on performance estimation methods. Machine Learning, 109(11), 1997–2028. https://doi.org/10.1007/s10994-020-05910-7
Chen, C. H., Lai, J. P., Chang, Y. M., Lai, C. J., & Pai, P. F. (2023). A Study of Optimization in Deep Neural Networks for Regression. Electronics (Switzerland), 12(14), 1–17. https://doi.org/10.3390/electronics12143071
Cronje, J. C. (2020). Designing Questions for Research Design and Design Research in e?Learning. Electronic Journal of E-Learning, 18(1), pp13-24.
Eom, H., & Choi, H. (2021). Alpha-Integration Pooling for Convolutional Neural Networks. Journal of KIISE, 48(7), 774–780. https://doi.org/10.5626/jok.2021.48.7.774
Fryer, L. K., & Dinsmore, D. L. (2020). The Promise and Pitfalls of Self-report: Development, research design and analysis issues, and multiple methods. Frontline Learning Research, 8(3), 1–9.
Goh, E., Yepremyan, A., Wang, J., & Wilson, B. (2023). MAESSTRO?: Masked Autoencoders for Sea Surface Temperature Reconstruction under Occlusion. July, 1–20.
Halmar, H. F. (2020). Pemeriksaan Diagnostik COVID-19. Jurnal Keperawatan Muhammadiyah, 5(1), 222–230.
Hariyani, Y. S., HADIYOSO, S., & SIADARI, T. S. (2020). Deteksi Penyakit Covid-19 Berdasarkan Citra X-Ray Menggunakan Deep Residual Network. ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika, 8(2), 443. https://doi.org/10.26760/elkomika.v8i2.443
Hassan, E., Shams, M. Y., Hikal, N. A., & Elmougy, S. (2023). The effect of choosing optimizer algorithms to improve computer vision tasks: a comparative study. In Multimedia Tools and Applications (Vol. 82, Issue 11). Multimedia Tools and Applications. https://doi.org/10.1007/s11042-022-13820-0
He, X., Xue, F., Ren, X., & You, Y. (2021). Large-Scale Deep Learning Optimizations: A Comprehensive Survey. 1(1). http://arxiv.org/abs/2111.00856
Hensman, P., & Masko, D. (2015). The Impact of Imbalanced Training Data for Convolutional Neural Networks. PhD. https://www.kth.se/social/files/588617ebf2765401cfcc478c/PHensmanDMasko_dkand15.pdf
Hidaka, A., & Kurita, T. (2017). Consecutive Dimensionality Reduction by Canonical Correlation Analysis for Visualization of Convolutional Neural Networks. Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and Its Applications, 2017(0), 160–167. https://doi.org/10.5687/sss.2017.160
Huang, L., Qin, J., Zhou, Y., Zhu, F., Liu, L., & Shao, L. (2023). Normalization Techniques in Training DNNs: Methodology, Analysis and Application. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–20. https://doi.org/10.1109/TPAMI.2023.3250241
Infante, M., Lutman, R. F., Imparato, S., Di Rocco, M., Ceresoli, G. L., Torri, V., Morenghi, E., Minuti, F., Cavuto, S., Bottoni, E., Inzirillo, F., Cariboni, U., Errico, V., Incarbone, M. A., Ferraroli, G., Brambilla, G., Alloisio, M., & Ravasi, G. (2009). Differential diagnosis and management of focal ground-glass opacities. European Respiratory Journal, 33(4), 821–827. https://doi.org/10.1183/09031936.00047908
Jepkoech, J., Mugo, D. M., Kenduiywo, B. K., & Too, E. C. (2021). The Effect of Adaptive Learning Rate on the Accuracy of Neural Networks. International Journal of Advanced Computer Science and Applications, 12(8), 736–751. https://doi.org/10.14569/IJACSA.2021.0120885
Jo, J.-M. (2019). Effectiveness of Normalization Pre-Processing of Big Data to the Machine Learning Performance. The Journal of the Korea Institute of Electronic Communication Sciences, 14, 547–552. https://doi.org/10.13067/JKIECS.2019.14.3.547
Mooijman, P., Catal, C., Tekinerdogan, B., Lommen, A., & Blokland, M. (2023). The effects of data balancing approaches: A case study. Applied Soft Computing, 132, 109853. https://doi.org/10.1016/j.asoc.2022.109853
Parsania, P. S., & Virparia, P. V. (2018). Computational Time Complexity of Image Interpolation Algorithms. International Journal of Computer Sciences and Engineering, 6(7), 491–496. https://doi.org/10.26438/ijcse/v6i7.491496
Pei, X., Zhao, Y. hong, Chen, L., Guo, Q., Duan, Z., Pan, Y., & Hou, H. (2023). Robustness of machine learning to color, size change, normalization, and image enhancement on micrograph datasets with large sample differences. Materials and Design, 232, 112086. https://doi.org/10.1016/j.matdes.2023.112086
Reyad, M., Sarhan, A., & Arafa, M. (2023). A modified Adam algorithm for deep neural network optimization. Neural Computing and Applications, 35, 1–18. https://doi.org/10.1007/s00521-023-08568-z
Sido, J., & Konopik, M. (2019). Deep Learning for Text Data on Mobile Devices. International Conference on Applied Electronics, 2019-Septe, 1–4. https://doi.org/10.23919/AE.2019.8867025
Sugihantono, A., Burhan, E., & Samoedra, E. (2020). Pedoman Pencegahan dan Pengendalian CORONAVIRUS DISEASE (COVID-19) Revisi ke-5. Jakarta: Kementerian Kesehatan Indonesia, 5(2), 1–214.
Susilo, A., Rumende, C. M., Pitoyo, C. W., Santoso, W. D., Yulianti, M., Herikurniawan, H., Sinto, R., Singh, G., Nainggolan, L., Nelwan, E. J., Chen, L. K., Widhani, A., Wijaya, E., Wicaksana, B., Maksum, M., Annisa, F., Jasirwan, C. O. M., & Yunihastuti, E. (2020). Coronavirus Disease 2019: Tinjauan Literatur Terkini. Jurnal Penyakit Dalam Indonesia, 7(1), 45. https://doi.org/10.7454/jpdi.v7i1.415
Swastika, W. (2020). Studi Awal Deteksi COVID-19 Menggunakan Citra CT Berbasis Deep Learning. Jurnal Teknologi Informasi Dan Ilmu Komputer, 7(3), 629. https://doi.org/10.25126/jtiik.2020733399
Yamashita, R., Nishio, M., Gian Do, R. K., & Togash, K. (2018). Convolutional neural networks: an overview and application in radiology. Smart Innovation, Systems and Technologies, 9, 611–629. https://doi.org/10.1007/978-981-15-7078-0_3
Yang, X., He, X., Zhao, J., Zhang, Y., Zhang, S., & Xie, P. (2020). COVID-CT-Dataset: A CT Scan Dataset about COVID-19. 1–14. http://arxiv.org/abs/2003.13865
Copyright and Licensing
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.