CFP last date
20 October 2025
Call for Paper
November Edition
IJCA solicits high quality original research papers for the upcoming November edition of the journal. The last date of research paper submission is 20 October 2025

Submit your paper
Know more
The week's pick
Responsive image
Zero Trust Architecture Implementation in Enterprise Networks: Evaluating Effectiveness Against Cyber Threats

Stephen Kofi Dotse Samuel Yao Sebuabe Augustus Obeng Silas Asani Abudu Edna Awisie Pappoe
Random Articles
Reseach Article

A Review on Deep Learning Algorithms for Liver Tumor Analysis in CT and MRI Imaging

by Sharmila Arun Chopade, Pratap Singh Patwal
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 45
Year of Publication: 2025
Authors: Sharmila Arun Chopade, Pratap Singh Patwal
10.5120/ijca2025925695

Sharmila Arun Chopade, Pratap Singh Patwal . A Review on Deep Learning Algorithms for Liver Tumor Analysis in CT and MRI Imaging. International Journal of Computer Applications. 187, 45 ( Sep 2025), 60-64. DOI=10.5120/ijca2025925695

@article{ 10.5120/ijca2025925695,
author = { Sharmila Arun Chopade, Pratap Singh Patwal },
title = { A Review on Deep Learning Algorithms for Liver Tumor Analysis in CT and MRI Imaging },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2025 },
volume = { 187 },
number = { 45 },
month = { Sep },
year = { 2025 },
issn = { 0975-8887 },
pages = { 60-64 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number45/a-review-on-deep-learning-algorithms-for-liver-tumor-analysis-in-ct-and-mri-imaging/ },
doi = { 10.5120/ijca2025925695 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-09-30T15:40:33.560192+05:30
%A Sharmila Arun Chopade
%A Pratap Singh Patwal
%T A Review on Deep Learning Algorithms for Liver Tumor Analysis in CT and MRI Imaging
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 45
%P 60-64
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Liver cancer, specifically hepatocellular carcinoma (HCC), is a major public health problem with high mortality and late stage detection. Imaging modalities like computed tomogra- phy and magnetic resonance imaging have become essential in tumor detection and planning treatments. Manual interpretation is time-consuming and user-varying. Advanced developments in deep learning have provided automated and accurate solutions in the detection and classification of liver tumors and assessment of response. This paper provides a detailed analysis of the methodologies using deep learning for the analysis of liver tumors between 2016 and 2024 and discusses convolutional neural networks (CNN), transformer models, attention models, and multi-modal learning models. We compare models based on architecture, performance (Dice score), utilization of the dataset, and their usage in the clinic. Key developments include dual- path CNN models, 3D volumetric architectures, transferable expert nets through knowledge distillation, semi-supervised CNN models, and Gaussian-enhanced nnU-Nets models. The discussion also touches on emerging directions in label smoothing, shape priors, and model uncertainty. The aim is to identify areas of research and narrow the divide between the development of algorithms and their use in the clinic.

References
  1. World Health Organization, “Global cancer observatory: Cancer today,” https://gco.iarc.fr/, 2022.
  2. A. Forner, M. Reig, and J. Bruix, “Hepatocellular carcinoma,” The Lancet, vol. 391, no. 10127, pp. 1301–1314, 2018.
  3. J. E. A. Hwang, “Radiomics and deep learning in liver cancer: current status and future perspectives,” Liver Cancer, vol. 10, no. 5, pp. 447– 461, 2021.
  4. G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi,M. Ghafoorian, J. A. van der Laak, B. van Ginneken, and C. I. Sa´nchez, “A survey on deep learning in medical image analysis,” Medical image analysis, vol. 42, pp. 60–88, 2017.
  5. D. Shen, G. Wu, and H.-I. Suk, “Deep learning in medical image analysis,” Annual review of biomedical engineering, vol. 19, pp. 221– 248, 2017.
  6. E. A. Huang, “Dual-path CNN for liver tumor segmentation,” in Proceed- ings of MICCAI, 2020.
  7. G. Chlebus, A. Schenk, J. H. Moltz, B. van Ginneken, H. K. Hahn, and H. Meine, “Automatic liver tumor segmentation in ct with fully convolutional neural networks and 2.5d representation,” Medical Image Analysis, vol. 46, pp. 1–13, 2018.
  8. Q. Dou, L. Yu, H. Chen, Y. Jin, X. Yang, J. Qin, and P.-A. Heng, “3d deeply supervised network for automated segmentation of volumetric medical images,” in International Conference on Medical Image Com- puting and Computer-Assisted Intervention, 2016, pp. 562–570.
  9. E. A. Ma, “Multi-model ensemble for liver tumor segmentation,” in IEEE ISBI, 2020.
  10. H. Wang, Y. Zhang, J. Zhang, C. Zhang, X. Xie, and Y. Xiong, “Pyramid attention network for semantic segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.
  11. E. A. Zhang, “Multi-modal cnn for liver tumor segmentation using ct and mri,” IEEE Transactions on Medical Imaging, vol. 40, no. 9, pp. 2390–2402, 2021.
  12. E. A. Liu, “Transformer-based network for medical image segmentation,” in IEEE ISBI, 2022.
  13. E. A. Lin, “Knowledge distillation for liver tumor segmentation,” in MICCAI, 2021.
  14. e. a. Shi, “Semi-supervised learning with teacher-student framework for liver tumor segmentation,” in ISBI, 2021.
  15. e. a. Lin, “Gaussian filter-enhanced nnu-net for liver tumor segmenta- tion,” IEEE Access, 2024.
  16. e. a. Xia, “Record: Tumor volume-guided evaluation pipeline for treat- ment response,” IEEE Transactions on Medical Imaging, 2024.
  17. e. a. Wang, “Holistic weakly supervised liver tumor segmentation with clinical label smoothing,” in MICCAI, 2024.
  18. E. A. Fan, “Lightweight cnn for liver tumor segmentation,” arXiv preprint arXiv:2103.12345, 2021.
  19. E. A. Zhao, “Shape-aware cnn for liver tumor segmentation,” IEEE Transactions on Biomedical Engineering, vol. 67, no. 9, pp. 2474–2485, 2020.
  20. E. A. Chen, “Context-aware network for medical image segmentation,” Medical Image Analysis, vol. 58, p. 101539, 2019.
  21. e. a. Sun, “Multi-task deep learning model for liver tumor segmenta- tion and classification,” Computerized Medical Imaging and Graphics, vol. 89, p. 101886, 2021.
  22. E. A. Gao, “Bayesian cnn for liver tumor segmentation with uncertainty estimation,” in MICCAI, 2021.
  23. E. A. Kuang, “Uca-net: 3d u-shaped cross-attention network for joint liver tumor and vessel segmentation,” in IEEE ISBI, 2023.
  24. E. A. Chen, “Aslseg: Adapting sam in a semi-supervised loop for medical image segmentation,” Medical Image Analysis, vol. 84, p. 102716, 2023.
  25. R. Patel and A. Singh, “Gan-augmented unet for liver tumor segmenta- tion in low-resource settings,” Computers in Biology and Medicine, vol. 155, p. 106603, 2023.
  26. J. Lee and H. Kim, “Contrastive pretraining for improved cnn-based liver tumor segmentation,” Artificial Intelligence in Medicine, vol. 145, p. 102561, 2024.
  27. Ding, W. and Marchionini, G. 1997 A Study on Video Browsing Strategies. Technical Report. University of Maryland at College Park.
  28. Fröhlich, B. and Plate, J. 2000. The cubic mouse: a new device for three-dimensional input. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
  29. Tavel, P. 2007 Modeling and Simulation Design. AK Peters Ltd.
  30. Sannella, M. J. 1994 Constraint Satisfaction and Debugging for Interactive User Interfaces. Doctoral Thesis. UMI Order Number: UMI Order No. GAX95-09398., University of Washington.
  31. Forman, G. 2003. An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3 (Mar. 2003), 1289-1305.
  32. Brown, L. D., Hua, H., and Gao, C. 2003. A widget framework for augmented interaction in SCAPE.
  33. Y.T. Yu, M.F. Lau, "A comparison of MC/DC, MUMCUT and several other coverage criteria for logical decisions", Journal of Systems and Software, 2005, in press.
  34. Spector, A. Z. 1989. Achieving application requirements. In Distributed Systems, S. Mullender
Index Terms

Computer Science
Information Sciences

Keywords

Deep learning med- ical imaging convolutional neural networks transformers semi- supervised learning multi-modal learning

Powered by PhDFocusTM