ArticleSensors (Basel, Switzerland)2022
Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 papers, 1 of them a synthesis that pooled it.
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Who cites it
80 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence Pipeline for Mammography-Based Breast Cancer Detection: An Integrated Systematic Review and Large-Scale Experimental Validation.Medicina (Kaunas, Lithuania) · 2025Pooled it
- Hybrid adaptive attention deep supervision-guided U-Net for breast lesion segmentation in ultrasound computed tomography images.Medical & biological engineering & computing · 2026Article
- A deep learning system for non-invasive breast cancer diagnosis with multimodal data.Nature biomedical engineering · 2026Article
- Favoring flat minima improves generalization of transfer-learned models for breast ultrasound tumor classification.Scientific reports · 2026Article
- Ensemble-based high-performance deep learning models for medical image retrieval in breast cancer detection.Scientific reports · 2026Article
- A Data-Efficient Machine Learning Approach for Breast Ultrasound Lesion Classification Integrating Image-Derived Features and Sonographic Descriptors.Diagnostics (Basel, Switzerland) · 2026Article
- Explainable AI in breast cancer ultrasound imaging: current developments and challenges.Frontiers in digital health · 2026Review
- Multi-scale information bottleneck with confidence-weighted decision fusion for robust breast ultrasound lesion classification.Frontiers in oncology · 2026Article
- Breast cancer inter-image dissimilarity by feature optimization: An application of novel flea optimization algorithm.PloS one · 2026Article
- The Current Research Landscape on Integrating Artificial Intelligence with Ultrasound Imaging for Cancer Diagnosis: A Dual-Database Bibliometric Study.Current medicinal chemistry · 2026Article
- Automated breast cancer diagnosis via intensity-guided contour segmentation and ResNet-based classification using local intensity pattern tensors.Scientific reports · 2025Article
- A Dual-stage Deep Learning Framework for Breast Ultrasound Image Segmentation and Classification.Journal of medical systems · 2025Article
- NeoNet: A Novel Deep Learning Model for Retinal Disease Diagnosis and Localization.Sensors (Basel, Switzerland) · 2025Article
- Multi-Class Classification of Breast Ultrasound Images Using Vision Transformer-Based Ensemble Learning.Diagnostics (Basel, Switzerland) · 2025Article
- Variational mode directed deep learning framework for breast lesion classification using ultrasound imaging.Scientific reports · 2025Article
- Quantitative Evaluation of Tendon Gliding Sounds and Their Classification Using Deep Learning Models.Cureus · 2025Article
- Ultrasound-based deep learning radiomics for multi-stage assisted diagnosis in reducing unnecessary biopsies of BI-RADS 4A lesions.Quantitative imaging in medicine and surgery · 2025Article
- Innovative breast cancer detection using a segmentation-guided ensemble classification framework.Biomedical engineering letters · 2025Article
- HED-Net: a hybrid ensemble deep learning framework for breast ultrasound image classification.Frontiers in artificial intelligence · 2025Article
- Ultrasound elastography features as promising predictors of pathological complete response in patients with HER2-positive breast cancer undergoing neoadjuvant therapy.American journal of translational research · 2025Article
20 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
After lung cancer, breast cancer is the second leading cause of death in women. If breast cancer is detected early, mortality rates in women can be reduced. Because manual breast cancer diagnosis takes a long time, an automated system is required for early cancer detection. This paper proposes a new framework for breast cancer classification from ultrasound images that employs deep learning and the fusion of the best selected features. The proposed framework is divided into five major steps: (i) data augmentation is performed to increase the size of the original dataset for better learning of Convolutional Neural Network (CNN) models; (ii) a pre-trained DarkNet-53 model is considered and the output layer is modified based on the augmented dataset classes; (iii) the modified model is trained using transfer learning and features are extracted from the global average pooling layer; (iv) the best features are selected using two improved optimization algorithms known as reformed differential evaluation (RDE) and reformed gray wolf (RGW); and (v) the best selected features are fused using a new probability-based serial approach and classified using machine learning algorithms. The experiment was conducted on an augmented Breast Ultrasound Images (BUSI) dataset, and the best accuracy was 99.1%. When compared with recent techniques, the proposed framework outperforms them.
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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.