ReviewDiagnostics (Basel, Switzerland)2023
Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction.
Review in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
55 citing papers in PubMed, 1 synthesis or guideline pooled it, 197 citations in OpenAlex.
- Artificial intelligence in breast cancer survival prediction: a comprehensive systematic review and meta-analysis.Frontiers in oncology · 2024Pooled it
- The Influence of Large Language Model-Generated Artificial Intelligence Risk Estimates on Clinical Decision-Making in Breast Imaging: A Simulation-Based Study.Diagnostics (Basel, Switzerland) · 2026Article
- Ultrasound Radiomics for Hashimoto's Thyroiditis: State of the Art and Future Perspectives.Journal of clinical ultrasound : JCU · 2026Review
- Bilateral-Asymmetry-Aware Multiple-Instance Learning for Breast Cancer Classification on Chest CT.Life (Basel, Switzerland) · 2026Article
- A Two-Stage Ensemble Machine Learning Pipeline for Breast Cancer Diagnosis from Digital Mammograms.Journal of imaging · 2026Article
- Integrating DNA methylation biomarkers for breast cancer risk prediction using artificial intelligence.Scientific reports · 2026Article
- Clinical Use of Infrared Thermography: Where Are We and Where Are We Going.Medicina (Kaunas, Lithuania) · 2026Article
- AI-Driven Breast Cancer Diagnosis: A Systematic Review of Imaging Modalities, Deep Learning, and Explainability.Cancers · 2026Review
- Review
- A sustainable ROS cycle generator (SRCG) for tumor chemodynamic therapy.Materials today. Bio · 2026Article
- Improving the efficacy and targeting of letrozole for the control of breast cancer: in vitro and in vivo studies.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Radiomics-integrated machine learning framework for quantitative breast cancer diagnosis.Frontiers in artificial intelligence · 2026Article
- Breast cancer inter-image dissimilarity by feature optimization: An application of novel flea optimization algorithm.PloS one · 2026Article
- Emerging Nanozyme Strategies for Precision Breast Cancer Treatment.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Hybrid Synthetic Minority Over-sampling Technique (HSMOTE) and Ensemble Deep Dynamic Classifier Model (EDDCM) for big data analytics.Scientific reports · 2025Article
- Evaluating ChatGPT's recommendations for systematic treatment decisions in recurrent or metastatic head and neck squamous cell carcinoma: Perspectives from experts and junior doctors.International journal of cancer · 2025Article
- Improving lung cancer detection with enhanced convolutional sequential networks.Scientific reports · 2025Article
- Deep learning-driven multi-omics analysis: enhancing cancer diagnostics and therapeutics.Briefings in bioinformatics · 2025Review
- A deep learning-based multimodal medical imaging model for breast cancer screening.Scientific reports · 2025Article
- Artificial intelligence utilization in cancer screening program across ASEAN: a scoping review.BMC cancer · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is one of the precarious conditions that affect women, and a substantive cure has not yet been discovered for it. With the advent of Artificial intelligence (AI), recently, deep learning techniques have been used effectively in breast cancer detection, facilitating early diagnosis and therefore increasing the chances of patients' survival. Compared to classical machine learning techniques, deep learning requires less human intervention for similar feature extraction. This study presents a systematic literature review on the deep learning-based methods for breast cancer detection that can guide practitioners and researchers in understanding the challenges and new trends in the field. Particularly, different deep learning-based methods for breast cancer detection are investigated, focusing on the genomics and histopathological imaging data. The study specifically adopts the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), which offer a detailed analysis and synthesis of the published articles. Several studies were searched and gathered, and after the eligibility screening and quality evaluation, 98 articles were identified. The results of the review indicated that the Convolutional Neural Network (CNN) is the most accurate and extensively used model for breast cancer detection, and the accuracy metrics are the most popular method used for performance evaluation. Moreover, datasets utilized for breast cancer detection and the evaluation metrics are also studied. Finally, the challenges and future research direction in breast cancer detection based on deep learning models are also investigated to help researchers and practitioners acquire in-depth knowledge of and insight into the area.
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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.