ArticleBiomedicines2022
A Hybrid Workflow of Residual Convolutional Transformer Encoder for Breast Cancer Classification Using Digital X-ray Mammograms.
Article in Biomedicines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 63 citations in OpenAlex.
- Application of deep learning technology in breast cancer: a systematic review of segmentation, detection, and classification approaches.Biomedical engineering online · 2026Pooled it
- Locate Then Classify: A Heterogeneous Dual-Teacher Consensus Framework for Semi-Supervised Mammogram Diagnosis.Journal of imaging informatics in medicine · 2026Article
- A novel hierarchical transfer learning to enhance breast cancer detection on screening mammography using deep learning.Physical and engineering sciences in medicine · 2026Article
- Improving IoT security through an explainable hybrid CNN-transformer model and federated learning.Scientific reports · 2026Article
- Hybrid diagnostic framework for bone cancer detection using deep learning and radiomics analysis.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
- An Interpretable Ensemble Transformer Framework for Breast Cancer Detection in Ultrasound Images.Diagnostics (Basel, Switzerland) · 2026Article
- Predicting tourism growth in Saudi Arabia with machine learning models for vision 2030 perspective.Scientific reports · 2026Article
- Cross-attention guided multi-modal network for breast ultrasound diagnosis incorporating objective clinical semantics.Frontiers in medicine · 2026Article
- LENS: A mammography-specific hybrid CNN-Transformer with lesion-aware evidence modeling.PloS one · 2026Article
- HMC-net: a ResNet fused hierarchical multi-scale cross-attention architecture for mammographic breast malignancy recognition incorporating explainable AI.Frontiers in oncology · 2026Article
- Explainable Mammogram Analysis with EfficientNetV2 and Grad-CAM++ for Robust Cancer Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- A hybrid vision transformer with ensemble CNN framework for cervical cancer diagnosis.BMC medical informatics and decision making · 2025Article
- BCECNN: an explainable deep ensemble architecture for accurate diagnosis of breast cancer.BMC medical informatics and decision making · 2025Article
- A hybrid explainable federated-based vision transformer framework for breast cancer prediction via risk factors.Scientific reports · 2025Article
- A hybrid segmentation and classification CAD framework for automated myocardial infarction prediction from MRI images.Scientific reports · 2025Article
- Surface defect detection on industrial drum rollers: Using enhanced YOLOv8n and structured light for accurate inspection.PloS one · 2025Article
- Self-supervised learning and transformer-based technologies in breast cancer imaging.Frontiers in radiology · 2025Review
- Diagnostic capabilities of artificial intelligence as an additional reader in a breast cancer screening program.European radiology · 2024Article
- A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis.Bioengineering (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 3 countries.
Funding
Abstract
Breast cancer, which attacks the glandular epithelium of the breast, is the second most common kind of cancer in women after lung cancer, and it affects a significant number of people worldwide. Based on the advantages of Residual Convolutional Network and the Transformer Encoder with Multiple Layer Perceptron (MLP), this study proposes a novel hybrid deep learning Computer-Aided Diagnosis (CAD) system for breast lesions. While the backbone residual deep learning network is employed to create the deep features, the transformer is utilized to classify breast cancer according to the self-attention mechanism. The proposed CAD system has the capability to recognize breast cancer in two scenarios:
Indexed as
Identifiers
What OpenQuestion holds
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.