Open Access

REVIEW

Next-Generation Deep Learning Approaches for Kidney Tumor Image Analysis: Challenges, Clinical Applications, and Future Perspectives

Neethu Rose Thomas^1,2, J. Anitha², Cristina Popirlan³, Claudiu-Ionut Popirlan³, D. Jude Hemanth^2,*

1 Department of Electronics and Communication Engineering, Jyothi Engineering College, Thrissur, 679531, India
2 Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences, Coimbatore, 641114, India
3 Department of Computer Science, University of Craiova, Craiova, 200585, Romania

* Corresponding Author: D. Jude Hemanth. Email:

Computers, Materials & Continua 2025, 85(3), 4407-4440. https://doi.org/10.32604/cmc.2025.070689

Received 21 July 2025; Accepted 19 September 2025; Issue published 23 October 2025

Abstract

Integration of artificial intelligence in image processing methods has significantly improved the accuracy of the medical diagnostics pathway for early detection and analysis of kidney tumors. Computer-assisted image analysis can be an effective tool for early diagnosis of soft tissue tumors located remotely or in inaccessible anatomical locations. In this review, we discuss computer-based image processing methods using deep learning, convolutional neural networks (CNNs), radiomics, and transformer-based methods for kidney tumors. These techniques hold significant potential for automated segmentation, classification, and prognostic estimation with high accuracy, enabling more precise and personalized treatment planning. Special focus is given to Vision Transformers (ViTs), Explainable AI (XAI), Federated Learning (FL), and 3D kidney image analysis. Additionally, the strengths and limitations of the established models are compared with recent techniques to understand both clinical and computational challenges that remain unresolved. Finally, the future directions for enhancing diagnostic precision, streamlining physician workflows, and image-guided intervention for decision support are proposed.

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