ReviewCancers2021
A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis.
Review in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
23 citing papers in PubMed.
- PEAO: a bio-inspired parallel optimizer with a multi-strategy communication mechanism for breast cancer diagnosis.Scientific reports · 2026Article
- Unlocking precision diagnostics: A multimodal framework integrating metabolomics with advanced machine learning techniques.PloS one · 2026Article
- A 2.5D multichannel deep learning model using contrast-enhanced ultrasound for predicting malignancy in breast nodules: a two-center study.Frontiers in physiology · 2026Article
- Explainable AI based hybrid DRM-Net transfer learning model for breast cancer detection and classification using ultrasound images.Scientific reports · 2025Article
- Application of CAD Systems in Breast Cancer Diagnosis Using Machine Learning Techniques: An Overview of Systematic Reviews.Bioengineering (Basel, Switzerland) · 2025Review
- Breast Cancer Detection Using Convolutional Neural Networks: A Deep Learning-Based Approach.Cureus · 2025Review
- Advanced Deep Learning Approaches in Detection Technologies for Comprehensive Breast Cancer Assessment Based on WSIs: A Systematic Literature Review.Diagnostics (Basel, Switzerland) · 2025Review
- Deep Learning and Radiomics in Triple-Negative Breast Cancer: Predicting Long-Term Prognosis and Clinical Outcomes.Journal of multidisciplinary healthcare · 2025Review
- Emerging research trends in artificial intelligence for cancer diagnostic systems: A comprehensive review.Heliyon · 2024Review
- Attention-Based Deep Learning Approach for Breast Cancer Histopathological Image Multi-Classification.Diagnostics (Basel, Switzerland) · 2024Article
- Conditional generative adversarial network driven radiomic prediction of mutation status based on magnetic resonance imaging of breast cancer.Journal of translational medicine · 2024Article
- Revolutionizing Breast Cancer Diagnosis: A Concatenated Precision through Transfer Learning in Histopathological Data Analysis.Diagnostics (Basel, Switzerland) · 2024Article
- DF-dRVFL: A novel deep feature based classifier for breast mass classification.Multimedia tools and applications · 2024Article
- Usefulness of machine learning and deep learning approaches in screening and early detection of breast cancer.Heliyon · 2023Review
- Review
- Vision-Transformer-Based Transfer Learning for Mammogram Classification.Diagnostics (Basel, Switzerland) · 2023Article
- Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction.Diagnostics (Basel, Switzerland) · 2023Review
- Deep ensemble learning and transfer learning methods for classification of senescent cells from nonlinear optical microscopy images.Frontiers in chemistry · 2023Article
- BUViTNet: Breast Ultrasound Detection via Vision Transformers.Diagnostics (Basel, Switzerland) · 2022Article
- Elimination of Defects in Mammograms Caused by a Malfunction of the Device Matrix.Journal of imaging · 2022Article
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.
Funding
Abstract
Breast cancer is now the most frequently diagnosed cancer in women, and its percentage is gradually increasing. Optimistically, there is a good chance of recovery from breast cancer if identified and treated at an early stage. Therefore, several researchers have established deep-learning-based automated methods for their efficiency and accuracy in predicting the growth of cancer cells utilizing medical imaging modalities. As of yet, few review studies on breast cancer diagnosis are available that summarize some existing studies. However, these studies were unable to address emerging architectures and modalities in breast cancer diagnosis. This review focuses on the evolving architectures of deep learning for breast cancer detection. In what follows, this survey presents existing deep-learning-based architectures, analyzes the strengths and limitations of the existing studies, examines the used datasets, and reviews image pre-processing techniques. Furthermore, a concrete review of diverse imaging modalities, performance metrics and results, challenges, and research directions for future researchers is presented.
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.