Conference Paper2023Cited by 13

Kidney Tumor Recognition from Abdominal CT Images using Transfer Learning

Sefatul Wasi, Saadia Binte Alam, Rashedur Rahman, M Ashraful Amin, Syoji Kobashi

2023 IEEE 53rd International Symposium on Multiple-Valued Logic (ISMVL)

IEEE, pp. 54–58

  • Good Health and Well-being — Relevance: 87%
DOI: 10.1109/ismvl57333.2023.00021URL

Abstract

Kidney tumor is a health concern that affects kidney cells and may leads to mortality depending on their type. Benign tumors can be unproblematic whereas malignant tumors pose the threat of kidney cancer. Early detection and diagnosis are possible through kidney tumor recognition based on deep learning techniques. In this paper, a method based on transfer learning using deep convolutional neural network (DCNN) is proposed to recognize kidney tumor from computed tomography (CT) images. The proposed method was evaluated on 5284 images. The final accuracy, precision, recall, specificity and F1 score were 92.54%, 80.45%, 93.02%, 92.38% and 0.8628, respectively.

Keywords

  • transfer learning
  • deep convolutional neural networks
  • abdominal CT images
  • Kidney cancer
  • tumor classification
  • early diagnosis

CCDS Authors

Saadia Binte Alam, PhD
Saadia Binte Alam, PhD

Saadia Binte Alam, PhD

  • Director, MIRA & WiSE Wings

Associate Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

HCII, Scientific Reports, Cognitive Computation, Neural Computing and Applications

Md. Rashedur Rahman, D. Eng.
Md. Rashedur Rahman, D. Eng.

Md. Rashedur Rahman, D. Eng.

  • Co-Director, MIRA & NEST Wings

Assistant Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

CHI, HCII, Scientific Reports, Cognitive Computation, Neural Computing and Applications

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Professor

+1 more affiliation

Department of Information Sciences and Technology

George Mason University, USA

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics

References

  1. 1.Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, Li Fei-Fei. (2009). ImageNet: A large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248–255[10.1109/cvpr.2009.5206848]
  2. 2.Kim Eun Hee, Alejandro Cosa‐Linan, Nandhini Santhanam, Mahboubeh Jannesari, Máté E. Maros, Thomas Ganslandt. (2022). Transfer learning for medical image classification: a literature review. BMC Medical Imaging, 22(1), 69[10.1186/s12880-022-00793-7]
  3. 3.Ross Whitaker. (1998). A Level-Set Approach to 3D Reconstruction from Range Data. International Journal of Computer Vision, 29(3), 203–231[10.1023/a:1008036829907]
  4. 4.Dandi Yang, Cristhian Martinez, Lara Visuña, Hardev Mukeshbhai Khandhar, Chintan Bhatt, Jesús Carretero. (2021). Detection and analysis of COVID-19 in medical images using deep learning techniques. Scientific Reports, 11(1), 19638[10.1038/s41598-021-99015-3]
  5. 5.Wenshuai Zhao, Dihong Jiang, Jorge Peña Queralta, Tomi Westerlund. (2020). MSS U-Net: 3D segmentation of kidneys and tumors from CT images with a multi-scale supervised U-Net. Informatics in Medicine Unlocked, 19, 100357[10.1016/j.imu.2020.100357]
  6. 6.Luana Batista da Cruz, José Denes Lima Araújo, Jonnison Lima Ferreira, João Otávio Bandeira Diniz, Aristófanes Corrêa Silva, João Dallyson Sousa de Almeida, Anselmo Cardoso de Paiva, Marcelo Gattass. (2020). Kidney segmentation from computed tomography images using deep neural network. Computers in Biology and Medicine, 123, 103906[10.1016/j.compbiomed.2020.103906]
  7. 7.Qian Yu, Yinghuan Shi, Jinquan Sun, Yang Gao, Jianbing Zhu, Yakang Dai. (2019). Crossbar-Net: A Novel Convolutional Neural Network for Kidney Tumor Segmentation in CT Images. IEEE Transactions on Image Processing, 28(8), 4060–4074[10.1109/tip.2019.2905537]
  8. 8.F. J. Turk, Murat Lüy, Necaattin Barışçı. (2020). Kidney and Renal Tumor Segmentation Using a Hybrid V-Net-Based Model. Mathematics, 8(10), 1772[10.3390/math8101772]
  9. 9.Fahad Parvez Mahdi, Kota Motoki, Syoji Kobashi. (2020). Optimization technique combined with deep learning method for teeth recognition in dental panoramic radiographs. Scientific Reports, 10(1), 19261[10.1038/s41598-020-75887-9]
  10. 10.Mohamed Shehata, Fahmi Khalifa, Ahmed Soliman, Rahaf Alrefai, Mohamed Abou El‐Ghar, Amy C. Dwyer, Rosemary Ouseph, Ayman El‐Baz. (2015). A level set-based framework for 3D kidney segmentation from diffusion MR images. , 4441–4445[10.1109/icip.2015.7351646]
  11. 11.Kazutoshi Ukai, Rashedur Rahman, Naomi Yagi, Keigo Hayashi, Akihiro Maruo, Hirotsugu Muratsu, Syoji Kobashi. (2021). Detecting pelvic fracture on 3D-CT using deep convolutional neural networks with multi-orientated slab images. Scientific Reports, 11(1), 11716[10.1038/s41598-021-91144-z]
  12. 12.Rui Lu, Pina Marziliano, Choon Hua Thng. (2005). Liver Tumor Volume Estimation By Semi-Automatic Segmentation Method. , 2005, 3296–3299[10.1109/iembs.2005.1617181]
  13. 13.Ting Chen, Dimitris Metaxas. (2005). A hybrid framework for 3D medical image segmentation. Medical Image Analysis, 9(6), 547–565[10.1016/j.media.2005.04.004]
  14. 14.Andrzej Skalski, Jacek Jakubowski, Tomasz Drewniak. (2016). Kidney tumor segmentation and detection on Computed Tomography data. , 238–242[10.1109/ist.2016.7738230]
  15. 15.Han Sang Lee, Helen Hong, Junmo Kim. (2017). Detection and segmentation of small renal masses in contrast-enhanced CT images using texture and context feature classification. [10.1109/isbi.2017.7950588]
  16. 16.Kyung-Sik Seo. (2005). Automatic Hepatic Tumor Segmentation Using Composite Hypotheses. Lecture notes in computer science, 922–929[10.1007/11559573_112]
  17. 17.Nader H. Abdel-massieh, Mohiy M. Hadhoud, Khalid Amin. (2010). Automatic liver tumor segmentation from CT scans with knowledge-based constraints. , 215–218[10.1109/cibec.2010.5716054]
  18. 18.Gareth Lewis, Alexander P. Maxwell. (2012). Early diagnosis improves survival in kidney cancer.. PubMed, 256(1748), 13–6, 2
  19. 19.Xi Yang, Jianpeng Zhang, Jing Zhang, Yong Xia. (2022). Transfer Learning for KiTS21 Challenge. Lecture notes in computer science, 158–163[10.1007/978-3-030-98385-7_21]
  20. 20.Jimin Heo. (2022). Automatic Segmentation in Abdominal CT Imaging for the KiTS21 Challenge. Lecture notes in computer science, 98–102[10.1007/978-3-030-98385-7_13]
  21. 21.Lizhan Xu, Jiacheng Shi, Zhangfu Dong. (2022). Modified nnU-Net for the MICCAI KiTS21 Challenge. Lecture notes in computer science, 22–27[10.1007/978-3-030-98385-7_3]
  22. 22.Karen Simonyan, Andrew Zisserman. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv (Cornell University)[10.48550/arxiv.1409.1556]

Cited By

  1. 1.Dhuha Abdalredha Kadhim, Mazin Abed Mohammed. (2024). A Comprehensive Review of Artificial Intelligence Approaches in Kidney Cancer Medical Images Diagnosis, Datasets, Challenges and Issues and Future Directions. International Journal of Mathematics Statistics and Computer Science, 1, 199–243[10.59543/ijmscs.v2i.9747]
  2. 2.A. Yoganathan, V. Dhivyadharshini, E. Loganathan, N. Prakash, C. A. Kandasamy, P. Kalyanasundaram. (2025). Improved Early Kidney Oncocytoma Identification in CT Scans using Hybrid CNN-RNN/Attention Models. International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), 80–86[10.1109/icicv64824.2025.11085847]
  3. 3.G. N. Balaji, A. K. P. Kovendan, Kirti Nayak, R. Venkatesan, D. Yuvaraj. (2024). Multi-Cancer Detection Using Deep Learning Techniques. Advances in medical technologies and clinical practice book series, 281–304[10.4018/979-8-3693-3719-6.ch014]
  4. 4.Siam Tahsin Bhuiyan, Rashedur Rahman, Saadia Binte Alam, Halima Khatun, Riyadul Islam, Sefatul Wasi, Md Akil Raihan Iftee, Ashraful Islam. (2025). Patch-Based Deep Ensemble Learning for Enhancing Pelvic Fracture Detection. International Conference on Computer and Information Technology (ICCIT), 3022–3027[10.1109/iccit68739.2025.11491331]
  5. 5.Heshan Chandeepa Pathmakumara, Gayan Perera. (2025). An Explainable AI-Based Ensemble Deep Learning Method for Precise Diagnosis of Kidney Disease from CT Images. International Conference on Information Technology Research (ICITR), 1–6[10.1109/icitr69413.2025.11353542]
  6. 6.S. Teena Mrudula, D. Jayaprakash Narayana, Sk. Muzammil Shareef, G. Leela Krishna. (2025). Multi-Task Learning Approach for Detecting Multiple Tumors Using CT and MRI Images. International Conference on Emerging Smart Computing and Informatics (ESCI), 1–6[10.1109/esci63694.2025.10988337]
  7. 7.Srinivas Aluvala, Pulluri Srinivas Rao, Keshoju Bhargavi, Vishali Sivalenka. (2024). A Deep Learning Model for Automated Kidney Disease Diagnosis Using CT Scan Images. IEEE Global Conference for Advancement in Technology (GCAT), 1–6[10.1109/gcat62922.2024.10923994]
  8. 8.Joyita Faruk, Saadia Binte Alam, Sk Sadia Tasnim Elma, Sefatul Wasi, Rashedur Rahman, Syoji Kobashi. (2024). Kidney Abnormality Detection Using Segmentation-Guided Classification on Computed Tomography Images. International Conference on Machine Learning and Cybernetics, 414–419[10.1109/icmlc63072.2024.10935113]
  9. 9.RN Uma Mahesh, D Likitha. (2026). Computed Tomography Kidney Disease Recognition using Deep Learning. IgMin Research, 4(8), 326–340[10.61927/igmin357]
  10. 10.Siam Tahsin Bhuiyan, Rashedur Rahman, Sefatul Wasi, Halima Khatun, Manosh Sur Chowdhury, Riyadul Islam, Samiul Karim Mazumder, Naomi Yagi, Syoji Kobashi, Saadia Binte Alam. (2026). Beyond the pelvic ring: benchmarking multi-bone segmentation for ROI-Guided fracture detection. Scientific Reports, 16(1)[10.1038/s41598-026-47663-8]
  11. 11.A Ravi Raja, K Meenakshi, K Priyanka, P Gnaneswararao, M Bindu Priya, Venkata Ratna Prabha K. (2026). Detection and Classification of Kidney Cancer using CT Images with ResNet-50 and Deployment on Raspberry Pi. International Conference on Electronics and Renewable Systems (ICEARS), 129–135[10.1109/icears67481.2026.11416518]
  12. 12.NiharikaVemula, Durgesh Nandan. (2025). EfficientNet-Based Deep Learning Framework for Automated Kidney Disease Detection in CT Imaging. International Conference on Recent Trends in Electrical, Electronics and Computing Technologies (ICRTEECT), 1–7[10.1109/icrteect67512.2025.11448771]
  13. 13.Rosy Salomi Victoria Daniel, Ashwin Roberts Rajesh, Gokul Saravanan, Gowtham Ganeshkumar, Arulan Sabari Kasinathan. (2024). Renal Irregularities Detection Using Convolutional Neural Network. IFIP advances in information and communication technology, 144–156[10.1007/978-3-031-73617-9_12]
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