Combination of VGG19 (Encoder) and U-Net (Decoder) for Colorectal Polyp Segmentation Image
DOI:
https://doi.org/10.30595/juita.v14i1.25783Keywords:
Colorectal polyp; segmentation image; U-Net; VGG19.Abstract
Health involves the proper function of the body and organs, with colon polyps being a common issue. Doctors often face challenges in segmenting medical images, especially endoscopic images for polyp detection. The complexity and variation in the appearance of polyps make accurate identification challenging, and the subjective manual segmentation process can result in misdiagnosis or delayed treatment. This study examines the effectiveness of the combination of U-Net decoder model architecture and VGG19 encoder in segmentation of colon polyp images. This study uses a public dataset, namely Kvasir-Seg with a total of 1000 images of colon polyps. An innovative approach using VGG19 as encoder and U-Net as decoder improves colorectal polyp segmentation, achieving high performance with a Loss of 0.05, Accuracy 0.95, Precision 0.96, Recall 0.92, IoU 0.89, and Dice 0.94. Using optimal parameters such as Nadam Optimizer, 5 Fold Cross Validation, Learning Rate 0.0001, and 25 Epochs significantly improved performance, increasing the Dice Coefficient to 0.92 and IoU to 0.86 compared to previous studies. This study concludes that the proposed architecture is reliable for colon polyp segmentation. Future work should explore attention mechanisms or transformer-based models to enhance accuracy and efficiency.
References
[1] K. Danna and R. W. Griffin, “Health and well-being in the workplace: A review and synthesis of the literature,” J Manage, vol. 25, no. 3, pp. 357–384, 1999, doi: 10.1177/014920639902500305.
[2] S. K. T. Seraglio, M. Schulz, L. V. Gonzaga, R. Fett, and A. C. O. Costa, “Current status of the gastrointestinal digestion effects on honey: A comprehensive review,” Sep. 30, 2021, Elsevier Ltd. doi: 10.1016/j.foodchem.2021.129807.
[3] Y. Lafau, Z. Azmi, and A. Calam, “Sistem Pakar Mendiagnosis Polip Usus Pada Manusia Menggunakan Metode Certainty Factor,” vol. 3, no. 5, pp. 602–609, 2024, [Online]. Available: https://ojs.trigunadharma.ac.id/index.php/jsi
[4] U. Athiyah, I. Muhimmah, and E. Marfianti, “Ekstraksi Ciri Polip dan Pendarahan Berdasarkan Citra Endoskopi Kolorektal,” Jurnal Informatika: Jurnal Pengembangan IT (JPIT), vol. 03, no. 01, 2018.
[5] D. Dornblaser, S. Young, and A. Shaukat, “Colon polyps: Updates in classification and management,” Jan. 01, 2024, Lippincott Williams and Wilkins. doi: 10.1097/MOG.0000000000000988.
[6] Y. Tudela, M. Majó, N. de la Fuente, A. Galdran, A. Krenzer, F. Puppe, A. Yamlahi, T. Nuong Tran, B. J. Matuszewski, K. Fitzgerald, C. Bian, J. Pan, S. Liu, G. Fernández-Esparrach, A. Histace, J. Bernal., “A complete benchmark for polyp detection, segmentation and classification in colonoscopy images,” Front Oncol, vol. 14, 2024, doi: 10.3389/fonc.2024.1417862.
[7] J. A. Yani, and U. Athiyah, “Pengenalan Polip Usus Menggunakan Neural Network”. Jurnal, I. Komputer, 2024.
[8] K. Wickstrom, M. Kampffmeyer, and R. Jenssen, “Uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps,” Medical Image Analysis, vol. 60, Feb. 2020, doi: 10.1016/j.media.2019.101619.
[9] S. Hosseinzadeh Kassani, P. Hosseinzadeh Kassani, M. J. Wesolowski, K. A. Schneider, and R. Deters, “Automatic Polyp Segmentation Using Convolutional Neural Networks,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, 2020, pp. 290–301. doi: 10.1007/978-3-030-47358-7_29.
[10] J. P. Horwath, D. N. Zakharov, R. Mégret, and E. A. Stach, “Understanding important features of deep learning models for segmentation of high-resolution transmission electron microscopy images,” NPJ Comput Mater, vol. 6, no. 1, Dec. 2020, doi: 10.1038/s41524-020-00363-x.
[11] J. Ma, Y. He, F. Li, L. Han, C. You, and B. Wang, “Segment anything in medical images,” Nat Commun, vol. 15, no. 1, Dec. 2024, doi: 10.1038/s41467-024-44824-z.
[12] A. M. Azor, D. J. Sharp, A. E. Jolly, N. J. Bourke, and P. J. Hellyer, “Automation and standardization of subject-specific region-of-interest segmentation for investigation of diffusion imaging in clinical populations,” PLoS One, vol. 17, no. 12 December, Dec. 2022, doi: 10.1371/journal.pone.0268233.
[13] A. Mirza and R. K. Rajak, “Segmentation of Polyp Instruments using UNet based deep learning model,” Nordic Machine Intelligence, vol. 1, no. 1, pp. 44–46, Nov. 2021, doi: 10.5617/nmi.9145.
[14] M. Yeung, E. Sala, C. B. Schönlieb, and L. Rundo, “Focus U-Net: A novel dual attention-gated CNN for polyp segmentation during colonoscopy,” Comput Biol Med, vol. 137, Oct. 2021, doi: 10.1016/j.compbiomed.2021.104815.
[15] D. Jha, P. Helén Smedsrud, D. Johansen, T. de Lange., “A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation,” Jul. 2021, [Online]. Available: http://arxiv.org/abs/2107.12435
[16] D. Jha, P. H. Smedsrud, M. A. Riegler, P. Halvorsen, T. de Lange, D. Johansen, H. D. Johansen., “Kvasir-SEG: A Segmented Polyp Dataset,” Nov. 2019, [Online]. Available: http://arxiv.org/abs/1911.07069
[17] N. Sharma, S. Gupta, D. Gupta, P. Gupta , S. Juneja, A. Shah, A. Shaikh., “UMobileNetV2 model for semantic segmentation of gastrointestinal tract in MRI scans,” PLoS One, vol. 19, no. 5 May, May 2024, doi: 10.1371/journal.pone.0302880.
[18] L. Mardiana, D. Kusnandar, and N. Satyahadewi, “Analisis Diskriminan Dengan K Fold Cross Validation Untuk Klasifikasi Kualitas Air Di Kota Pontianak,” 2022.
[19] M. A. Rajab, F. A. Abdullatif, and T. Sutikno, “Classification of grapevine leaves images using VGG-16 and VGG-19 deep learning nets,” Telkomnika (Telecommunication Computing Electronics and Control), vol. 22, no. 2, pp. 445–453, 2024, doi: 10.12928/TELKOMNIKA.v22i2.25840.
[20] W. I. Kusumawati and A. Z. Noorizki, “Perbandingan Performa Algoritma VGG16 Dan VGG19 Melalui Metode CNN Untuk Klasifikasi Varietas Beras,” Journal of Computer, Electronic, and Telecommunication, vol. 4, no. 2, Dec. 2023, doi: 10.52435/complete.v4i2.387.
[21] P. D. Wulaning Ayu and G. A. Pradipta, “U-Net Tuning Hyperparameter for Segmentation in Amniotic Fluid Ultrasonography Image,” in 2022 4th International Conference on Cybernetics and Intelligent System, ICORIS 2022, Institute of Electrical and Electronics Engineers Inc., 2022. doi: 10.1109/ICORIS56080.2022.10031294.
[22] O. Hospodarskyy, V. Martsenyuk, N. Kukharska, A. Hospodarskyy, and S. Sverstiuk, “Understanding the Adam Optimization Algorithm in Machine Learning,” 2024.
[23] Z. Zhixuan and H. Zaien, “Research and Application of Rectified-NAdam Optimization Algorithm in Data Classification,” American Journal of Computer Science and Technology, vol. 4, no. 4, p. 106, 2021, doi: 10.11648/j.ajcst.20210404.13.
[24] A. Khozaimi and W. Firdaus Mahmudy, “New insight in cervical cancer diagnosis using convolution neural network architecture,” IAES International Journal of Artificial Intelligence (IJ-AI), vol. 13, no. 3, pp. 3092–3100, 2024, doi: 10.11591/ijai.v.
[25] Liu, Jinlan, Xu, Dongpo, Zhang, M. Huisheng, Danilo., “On hyper-parameter selection for guaranteed convergence of RMSProp,” Cogn Neurodyn, 2022.
[26] P. Fan, Y. Diao, F. Li, W. Zhao, and Z. Chen, “SRSegNet: Super-resolution-assisted small targets polyp segmentation network with combined high and low resolution,” Journal of King Saud University - Computer and Information Sciences, vol. 36, no. 3, Mar. 2024, doi: 10.1016/j.jksuci.2024.101981.
[27] R. Islam, R. S. Akash, M. A. Hossen Rony, and M. Z. Hasan, “SAMU-Net: A dual-stage polyp segmentation network with a custom attention-based U-Net and segment anything model for enhanced mask prediction,” Array, vol. 24, Dec. 2024, doi: 10.1016/j.array.2024.100370.
[28] P. D. W. Ayu, G. A. Pradipta, R. R. Huizen, E. S. W. Kadek, and I. G. E. Artana, “Combining CNN Feature Extractors and Oversampling Safe Level SMOTE to Enhance Amniotic Fluid Ultrasound Image Classification,” International Journal of Intelligent Engineering and Systems, vol. 17, no. 1, pp. 251–262, 2024, doi: 10.22266/ijies2024.0229.24.
[29] D. Rajasekar, G. Theja, M. R. Prusty, and S. Chinara, “Efficient colorectal polyp segmentation using wavelet transformation and AdaptUNet: A hybrid U-Net,” Heliyon, vol. 10, no. 13, Jul. 2024, doi: 10.1016/j.heliyon.2024.e33655.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 JUITA: Jurnal Informatika

This work is licensed under a Creative Commons Attribution 4.0 International License.

JUITA: Jurnal Informatika is licensed under a Creative Commons Attribution 4.0 International License.








