SavedAdd your journal

Journals / Turkish Journal of Forecasting / 2022 / Cilt: 06 - Sayı: 2

Fully Automatic End-to-End Convolutional Neural Networks-Based Pancreatic Tumor Segmentation on CT Modality

Ahmet Furkan BAYRAM
Caglar GURKAN
Abdulkadir BUDAK
Hakan KARATAŞ
Pages
67–72
Publication Date
2022-12-31
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Considering these results, an artificial intelligence-based decision support system was created in the study.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The pancreas is one of the vital organs in the human body.
📊
Key Findings & Data:In this way, the effects of pancreas diseases, especially pancreatic cancer on the person are decreased.
💡
Conclusion & Impact:Considering these results, an artificial intelligence-based decision support system was created in the study.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Pancreas · Pancreatic Tumor · Deep Learning

Abstract

The pancreas is one of the vital organs in the human body. Early diagnosis of a disease in the pancreas is critical. In this way, the effects of pancreas diseases, especially pancreatic cancer on the person are decreased. With this purpose, artificial intelligence-assisted pancreatic cancer segmentation was performed for early diagnosis in this paper. For this aim, several state-of-the-art segmentation networks, UNet, LinkNet, SegNet, SQ-Net, DABNet, EDANet, and ESNet were used in this study. In the comparative analysis, the best segmentation performance has been achieved by SQ-Net. SQ-Net has achieved a 0.917 dice score, 0.847 IoU score, 0.920 sensitivity, 1.000 specificity, 0.914 precision, and 0.999 accuracy. Considering these results, an artificial intelligence-based decision support system was created in the study.

🔖

Cite This Article

BAYRAM, A. F., GURKAN, C., BUDAK, A., KARATAŞ, H. (2022). Fully Automatic End-to-End Convolutional Neural Networks-Based Pancreatic Tumor Segmentation on CT Modality. Turkish Journal of Forecasting, Cilt: 06 - Sayı: 2, 67–72. https://ajindex.com/articles/870965

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (4)

  1. J.X. Hu, Y.Y. Lin, C.F. Zhao, W.B. Chen, Q.C. Liu, Q.W. Li, F. Gao, Pancreatic cancer: A review of epidemiology, trend, and risk factors, World J. Gastroenterol. 27 (2021) 4298–4321. doi:10.3748/wjg.v27.i27.4298. V. Chaudhary, S. Bano, Imaging of the pancreas: Recent advances, Indian J. Endocrinol. Metab. 15 (2011) 25. doi:10.4103/2230-8210.83060. Z. Liu, J. Su, R. Wang, R. Jiang, Y.Q. Song, D. Zhang, Y. Zhu, D. Yuan, Q. Gan, V.S. Sheng, Pancreas Co-segmentation based on dynamic ROI extraction and VGGU-Net, Expert Syst. Appl. 192 (2022) 116444. doi:10.1016/j.eswa.2021.116444. D. Zhang, J. Zhang, Q. Zhang, J. Han, S. Zhang, J. Han, Automatic pancreas segmentation based on lightweight DCNN modules and spatial prior propagation, Pattern Recognit. 114 (2021) 107762. doi:10.1016/j.patcog.2020.107762. O. Ronneberger, P. Fischer, T. Brox, U-net: Convolutional networks for biomedical image segmentation, in: Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), Springer Verlag, 2015: pp. 234–241. doi:10.1007/978-3-319-24574-4_28/COVER. A. Chaurasia, E. Culurciello, LinkNet: Exploiting encoder representations for efficient semantic segmentation, in: 2017 IEEE Vis. Commun. Image Process. VCIP 2017, Institute of Electrical and Electronics Engineers Inc., 2018: pp. 1–4. doi:10.1109/VCIP.2017.8305148. V. Badrinarayanan, A. Kendall, R. Cipolla, SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation, IEEE Trans. Pattern Anal. Mach. Intell. 39 (2017) 2481–2495. doi:10.1109/TPAMI.2016.2644615. M. Treml, J. Arjona-medina, T. Unterthiner, R. Durgesh, F. Friedmann, P. Schuberth, A. Mayr, M. Heusel, M. Hofmarcher, M. Widrich, B. Nessler, S. Hochreiter, Speeding up Semantic Segmentation for Autonomous Driving, NIPS 2016
  2. Work. MLITS. (2016) 1–7. https://openreview.net/pdf?id=S1uHiFyyg%0Ahttps://openreview.net/forum?id=S1uHiFyyg (accessed 14 October 2022).
  3. G. Li, J. Kim, DABNet: Depth-wise asymmetric bottleneck for real-time semantic segmentation, in: 30th Br. Mach. Vis. Conf. 2019, BMVC 2019, BMVA Press, 2020. doi:10.48550/arxiv.1907.11357. S.Y. Lo, H.M. Hang, S.W. Chan, J.J. Li, Efficient dense modules of asymmetric convolution for real-time semantic segmentation, in: 1st ACM Int. Conf. Multimed. Asia, MMAsia 2019,
  4. Association for Computing Machinery, Inc, 2019. doi:10.1145/3338533.3366558. Y. Wang, Q. Zhou, J. Xiong, X. Wu, X. Jin, ESNet: An efficient symmetric network for real-time semantic segmentation, i