ArticleJournal of healthcare engineering2021
Boosting Breast Cancer Detection Using Convolutional Neural Network.
Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 3 of them syntheses that pooled it.
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Who cites it
30 citing papers in PubMed, 3 syntheses or guidelines pooled it, 219 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
- Critical Analysis of the Current Medical Image-Based Processing Techniques for Automatic Disease Evaluation: Systematic Literature Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Blockchain and Fog Computing in IoT-Driven Healthcare Services for Smart Cities.Journal of healthcare engineering · 2022Pooled it
- MassSeg-Framework: A Breast Mass Detection and Segmentation Framework Based on Deep Learning and an Active Contour Model.Life (Basel, Switzerland) · 2026Article
- PatchSight-ImmuneMap-LifeSpan as a unified AI framework for breast cancer diagnosis, immune profiling and prognostic prediction.Discover oncology · 2026Article
- Grad-CAM based deep learning analytics for image-level colon disease classification based on graph neural networks and vision transformers.Frontiers in physiology · 2026Article
- AdaptiveInvolutionNet: Spatially-adaptive involution with channel-wise attention for breast MRI tumor classification.PloS one · 2026Article
- Emerging Nanozyme Strategies for Precision Breast Cancer Treatment.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Employing transfer learning for breast cancer detection using deep learning models.PLOS digital health · 2025Article
- Accurate bladder cancer diagnosis using ensemble deep leaning.Scientific reports · 2025Article
- Divulging Patterns: An Analytical Review for Machine Learning Methodologies for Breast Cancer Detection.Journal of Cancer · 2025Review
- Artificial Intelligence in Breast Cancer Diagnosis and Surgical Decision-Making: An Updated and Comprehensive Overview of Precision and Personalization in Current Evidence.Cancer management and research · 2025Review
- Early detection and analysis of accurate breast cancer for improved diagnosis using deep supervised learning for enhanced patient outcomes.PeerJ. Computer science · 2025Article
- Enhancing Breast Cancer Detection: A Machine Learning Approach Using Multielectrode Bioimpedance.Bioelectricity · 2024Article
- CNN-based deep learning approach for classification of invasive ductal and metastasis types of breast carcinoma.Cancer medicine · 2024Article
- Breast cancer detection employing stacked ensemble model with convolutional features.Cancer biomarkers : section A of Disease markers · 2024Article
- Histopathological Image Diagnosis for Breast Cancer Diagnosis Based on Deep Mutual Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Sensitivity of CNN image analysis to multifaceted measurements of neurite growth.BMC bioinformatics · 2023Article
- A New Deep-Learning-Based Model for Breast Cancer Diagnosis from Medical Images.Diagnostics (Basel, Switzerland) · 2023Article
- A review and comparative study of cancer detection using machine learning: SBERT and SimCSE application.BMC bioinformatics · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer forms in breast cells and is considered as a very common type of cancer in women. Breast cancer is also a very life-threatening disease of women after lung cancer. A convolutional neural network (CNN) method is proposed in this study to boost the automatic identification of breast cancer by analyzing hostile ductal carcinoma tissue zones in whole-slide images (WSIs). The paper investigates the proposed system that uses various convolutional neural network (CNN) architectures to automatically detect breast cancer, comparing the results with those from machine learning (ML) algorithms. All architectures were guided by a big dataset of about 275,000, 50 × 50-pixel RGB image patches. Validation tests were done for quantitative results using the performance measures for every methodology. The proposed system is found to be successful, achieving results with 87% accuracy, which could reduce human mistakes in the diagnosis process. Moreover, our proposed system achieves accuracy higher than the 78% accuracy of machine learning (ML) algorithms. The proposed system therefore improves accuracy by 9% above results from machine learning (ML) algorithms.
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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.