ORIGINAL RESEARCH article
Front. Bioeng. Biotechnol.
Sec. Biosensors and Biomolecular Electronics
Breast Tumor Segmentation and Morphological Feature-Based Classification in Ultrasound Using a Two-Stage U-Net and SVM
Yang Ye 1
Mingtao Ye 2
Huihui Wang 3
Jiayu Fang 3
Guodao Zhang 2
Genfu Yang 2
Shurong Shen 1
Xiaoyang Li 1
1. Wenzhou Central Hospital, Wenzhou, China
2. Hangzhou Dianzi University, Hangzhou, China
3. Wenzhou Medical University, Wenzhou, China
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Abstract
Breast cancer remains one of the most prevalent and life-threatening conditions among women worldwide, making early detection and accurate diagnosis essential. In this study, we present a two-stage computer-aided diagnosis (CAD) framework designed for the automated analysis of breast ultrasound images. The proposed system first employs a U-Net-based semantic segmentation model to detect and localize potential tumor regions. The model is trained and evaluated on a comprehensive dataset comprising normal, benign, and malignant cases. For each input image, the U-Net predicts a binary tumor mask; images with no detected tumor regions are classified as normal and excluded from further analysis. In the second stage, images identified as tumor-bearing undergo feature extraction to characterize the shape and morphology of the segmented tumor. Specifically, four handcrafted features—circularity, solidity, eccentricity, and extent—are computed from the predicted masks. These features are then used to train a support vector machine (SVM) classifier that distinguishes between benign and malignant tumors. The segmentation model achieved an average Mask Intersection over Union% (Mask IoU)intersection-over-union (IOU) score of 0.9191%, while the classification model reached an accuracy of 98.23% on the training set and 97.42% on the test set. Unlike end-to-end deep learning approaches that often function as black boxes with limited clinical interpretability, our two-stage framework combines accurate deep learning-based segmentation with lightweight, handcrafted morphological feature classification using SVM. This design achieves high performance while preserving explainability through clinically meaningful shape descriptors, making it particularly suitable for real-world clinical deployment. The combination of deep learning-based segmentation with lightweight, interpretable morphological classification offers a robust and explainable pipeline suitable for clinical breast cancer screening using ultrasound imaging.
Summary
Keywords
breast cancer, computer-aided diagnosis, Morphological features, Semantic segmentation, SVM, ultrasound imaging, U-net
Received
23 December 2025
Accepted
17 February 2026
Copyright
© 2026 Ye, Ye, Wang, Fang, Zhang, Yang, Shen and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Guodao Zhang
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.