ArticleHeliyon2023
Breast cancer classification based on convolutional neural network and image fusion approaches using ultrasound images.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Wavelet Spectrum in a Multi-Channel Network May Reduce Biopsy Rates on Diagnosis of Breast Tumors With BI-RADS Category 4a or Higher.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2026Article
- A Dual-Task Deep-Learning Model with Fused Ultrasound Images for Simultaneous Typing and Grading of Cystocele.International urogynecology journal · 2026Article
- Federated Privacy-Preserving Multi-Modal Deep Learning for Breast Cancer Diagnosis: A Physics-Aware Approach.Diagnostics (Basel, Switzerland) · 2026Article
- Favoring flat minima improves generalization of transfer-learned models for breast ultrasound tumor classification.Scientific reports · 2026Article
- Role of Three-Dimensional Convolution Neural Networks (3D-CNN) in Image Processing and Recognition in Oncology: A Systematic Review and Meta-Analysis.International journal of hematology-oncology and stem cell research · 2026Review
- Ensemble-based high-performance deep learning models for medical image retrieval in breast cancer detection.Scientific reports · 2026Article
- An Interpretable Ensemble Transformer Framework for Breast Cancer Detection in Ultrasound Images.Diagnostics (Basel, Switzerland) · 2026Article
- A segmentation-guided CNN-Vision transformer feature fusion framework for multi-class breast ultrasound image classification.PloS one · 2026Article
- Radiomics and Deep Learning: Bridging Breast Cancer Imaging Phenotypes and Genomic Heterogeneity.Breast cancer (Dove Medical Press) · 2026Review
- Efficient temporal feature utilization in ultrasound videos: a multi-channel deep learning framework for enhanced breast lesion differentiation.BMC cancer · 2025Article
- HyFusion-X: hybrid deep and traditional feature fusion with ensemble classifiers for breast cancer detection using mammogram and ultrasound images.Scientific reports · 2025Article
- Correlation of paracancerous area ultrasound characteristics and the 21-gene recurrence score: the clinical value for early-stage luminal A invasive breast cancer.Quantitative imaging in medicine and surgery · 2025Article
- Development of a clinical decision support system for breast cancer detection using ensemble deep learning.Scientific reports · 2025Article
- Smart neural network and cognitive computing process for multi task nuclei detection segmentation and classification in breast cancer histopathology images.Scientific reports · 2025Article
- An explainable AI-driven deep neural network for accurate breast cancer detection from histopathological and ultrasound images.Scientific reports · 2025Article
- Extracting Knowledge from Machine Learning Models to Diagnose Breast Cancer.Life (Basel, Switzerland) · 2025Article
- Explainable AI-enabled hybrid deep learning architecture for breast cancer detection.Frontiers in immunology · 2025Article
- HED-Net: a hybrid ensemble deep learning framework for breast ultrasound image classification.Frontiers in artificial intelligence · 2025Article
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Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning and image processing are used to classify and segment breast tumor images, specifically in ultrasound (US) modalities, to support clinical decisions and improve healthcare quality. However, directly using US images can be challenging due to noise and diverse imaging modalities. In this study, we developed a three-step image processing scheme involving speckle noise filtering using a block-matching three-dimensional filtering technique, region of interest highlighting, and RGB fusion. This method enhances the generalization of deep-learning models and achieves better performance. We used a deep learning model (VGG19) to perform transfer learning on three datasets: BUSI (780 images), Dataset B (162 images), and KAIMRC (5693 images). When tested on the BUSI and KAIMRC datasets using a fivefold cross-validation mechanism, the model with the proposed preprocessing step performed better than without preprocessing for each dataset. The proposed image processing approach improves the performance of the breast cancer deep learning classification model. Multiple diverse datasets (private and public) were used to generalize the model for clinical application.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.