Accurate Kidney Tumor Medical Image Segmentation Using Optimized U-Net Algorithm

Y. Divya; M. Shanmuga  Sundari; S. Manaswini; P. Rakshitha; G. Bhargavi Reddy

doi:10.2991/978-94-6463-858-5_278

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Accurate Kidney Tumor Medical Image Segmentation Using Optimized U-Net Algorithm

Authors

Y. Divya¹^{, *}, M. Shanmuga Sundari¹, S. Manaswini¹, P. Rakshitha¹, G. Bhargavi Reddy¹

¹BVRIT HYDERABAD College of Engineering for Women , Hyderabad, India

^*Corresponding author. Email: divya.y@bvrithyderabad.edu.in

Corresponding Author

Y. Divya

Available Online 4 November 2025.

DOI: 10.2991/978-94-6463-858-5_278 How to use a DOI?
Keywords: Kidney Tumor Segmentation; U-Net; Deep Learning; Medical Image Analysis; CT Scans; KiTS19 Dataset; Tumor Detection
Abstract: Kidney tumor segmentation and identification. This system detects tumor in digital images of kidneys by means of analysis. Precision tumorlocalisation is achieved using U-Net model, so improving the segmentation and detection flow. This method guarantees better performance in medical diagnostics as well as improved accuracy of tumor identification. The system’s capacity to precisely identify and localise tumors promises better early diagnosis, so supporting quick and efficient treatment. This work uses the KiTS19 dataset to classify and segment kidney tumors applying the U-Net architecture. Pre-processing, data augmentation, and training a U-Net model optimized for performance on a CPU environment constitute the suggested approach. The efficiency of the model in tumor identification is shown by the experimental results.
Copyright: © 2025 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title: Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
Series: Advances in Computer Science Research
Publication Date: 4 November 2025
ISBN: 978-94-6463-858-5
ISSN: 2352-538X
DOI: 10.2991/978-94-6463-858-5_278 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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ris enw bib

TY  - CONF
AU  - Y. Divya
AU  - M. Shanmuga  Sundari
AU  - S. Manaswini
AU  - P. Rakshitha
AU  - G. Bhargavi Reddy
PY  - 2025
DA  - 2025/11/04
TI  - Accurate Kidney Tumor Medical Image Segmentation Using Optimized U-Net Algorithm
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 3334
EP  - 3342
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_278
DO  - 10.2991/978-94-6463-858-5_278
ID  - Divya2025
ER  -

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