ArticleScientific reports2025
Explainable attention based breast tumor segmentation using a combination of UNet, ResNet, DenseNet, and EfficientNet models.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Synthetic-to-Clinical Ensemble Learning for Volumetric Breast Tumor Segmentation in Digital Breast Tomosynthesis Under Limited Annotated Data.Diagnostics (Basel, Switzerland) · 2026Article
- AI-Assisted Tumor Boundary Delineation via Targeted Ultrasmall Iron Oxide Nanoprobe for High-Contrast HER2-Positive Tumor Imaging.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- LTM-UNet: Linear Transformer-Mamba with Attention-Based U-Net for Context-Aware Breast Ultrasound Image Segmentation.Diagnostics (Basel, Switzerland) · 2026Article
- Multi-level semi-supervised GAN enables accurate segmentation of breast ultrasound images.Scientific reports · 2026Article
- Clinically Aware Learning: Ordinal Loss Improves Medical Image Classifiers.Journal of clinical medicine · 2026Article
- CAFrontiers in artificial intelligence · 2026Article
- Breast tumor segmentation and morphological feature-based classification in ultrasound using a two-stage U-net and SVM.Frontiers in bioengineering and biotechnology · 2026Article
- Attention guided convolutional neural network with explainable AI for papaya leaf disease detection in edge and drone agricultural systems.Scientific reports · 2025Article
- The role of data augmentation and attention mechanisms in UNet and ConvNeXt architectures for optimizing breast tumor segmentation.Scientific reports · 2025Article
- A hybrid attention network for accurate breast tumor segmentation in ultrasound images.Scientific reports · 2025Article
- From Machine Learning to Ensemble Approaches: A Systematic Review of Mammogram Classification Methods.Diagnostics (Basel, Switzerland) · 2025Review
- PUNet: a lightweight parallel U-Net architecture integrating Mamba-CNN for high-precision image segmentation.Scientific reports · 2025Article
- Optimizing YOLOv11 for automated classification of breast cancer in medical images.Scientific reports · 2025Article
- Computer vision to predict cell seeding coverage in re-endothelialized mouse lungs.Scientific reports · 2025Article
- Multi channel fusion diffusion models for brain tumor MRI data augmentation.Scientific reports · 2025Article
- Bridging the Gap Between Accuracy and Efficiency in AI-Based Breast Cancer Diagnosis from Histopathological Data.Cancers · 2025Article
- Fine-tuned deep learning models for early detection and classification of kidney conditions in CT imaging.Scientific reports · 2025Article
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5 authors.
Funding
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Abstract
This study utilizes the Breast Ultrasound Image (BUSI) dataset to present a deep learning technique for breast tumor segmentation based on a modified UNet architecture. To improve segmentation accuracy, the model integrates attention mechanisms, such as the Convolutional Block Attention Module (CBAM) and Non-Local Attention, with advanced encoder architectures, including ResNet, DenseNet, and EfficientNet. These attention mechanisms enable the model to focus more effectively on relevant tumor areas, resulting in significant performance improvements. Models incorporating attention mechanisms outperformed those without, as reflected in superior evaluation metrics. The effects of Dice Loss and Binary Cross-Entropy (BCE) Loss on the model's performance were also analyzed. Dice Loss maximized the overlap between predicted and actual segmentation masks, leading to more precise boundary delineation, while BCE Loss achieved higher recall, improving the detection of tumor areas. Grad-CAM visualizations further demonstrated that attention-based models enhanced interpretability by accurately highlighting tumor areas. The findings denote that combining advanced encoder architectures, attention mechanisms, and the UNet framework can yield more reliable and accurate breast tumor segmentation. Future research will explore the use of multi-modal imaging, real-time deployment for clinical applications, and more advanced attention mechanisms to further improve segmentation performance.
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