ReviewJournal of cancer research and clinical oncology2023
Machine learning and deep learning techniques for breast cancer diagnosis and classification: a comprehensive review of medical imaging studies.
Review in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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.
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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.
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Who cites it
29 citing papers in PubMed.
- Synthetic-to-Clinical Ensemble Learning for Volumetric Breast Tumor Segmentation in Digital Breast Tomosynthesis Under Limited Annotated Data.Diagnostics (Basel, Switzerland) · 2026Article
- Bilateral-Asymmetry-Aware Multiple-Instance Learning for Breast Cancer Classification on Chest CT.Life (Basel, Switzerland) · 2026Article
- Identifying Potential Exosome-Derived mRNA Biomarkers for Diagnosis and Prediction of Breast Cancer Using Machine-Learning Approaches.Interdisciplinary sciences, computational life sciences · 2026Article
- Automated generation of structured breast ultrasound reports using BreastViT and ChatGPT.BMC medical informatics and decision making · 2026Article
- Machine learning constructs a diagnostic prediction model for gangrenous perforation of acute appendicitis in elderly patients.BMC surgery · 2026Article
- BrCaM an artificial intelligence model for surgical decision making in breast cancer.Scientific reports · 2026Article
- Comparison of Interpretable Machine Learning Models Using Systemic Inflammation Index to Predict Preterm Birth in Gestational Diabetes Mellitus.International journal of women's health · 2026Article
- Monte Carlo Tree Search for optimal cancer intervention strategies among BRCA mutation carriers.Computers in biology and medicine · 2025Article
- Utilizing a publicly accessible automated machine learning platform to enable diagnosis before tumor surgery.Communications medicine · 2025Article
- Multimodal imaging and advanced quantitative techniques for HER-2 status prediction in breast cancer.Discover oncology · 2025Review
- A Cross-scale Attention-Based U-Net for Breast Ultrasound Image Segmentation.Journal of imaging informatics in medicine · 2025Article
- The Evolution and Clinical Impact of Deep Learning Technologies in Breast MRI.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Review
- Deep Learning-Enhanced T1-Weighted Imaging for Breast MRI at 1.5T.Diagnostics (Basel, Switzerland) · 2025Article
- Comparative analysis of deep learning architectures for breast region segmentation with a novel breast boundary proposal.Scientific reports · 2025Article
- Dual-branch dynamic hierarchical U-Net with multi-layer space fusion attention for medical image segmentation.Scientific reports · 2025Article
- Review
- Bi-modal ultrasound radiomics and habitat analysis enhanced the pre-operative prediction of axillary lymph node burden in patients with early-stage breast cancer.Frontiers in oncology · 2025Article
- Identifying clinically relevant findings in breast cancer using deep learning and feature attribution on local views from high-resolution mammography.Frontiers in oncology · 2025Article
- Article
- Brain tumor classification utilizing pixel distribution and spatial dependencies higher-order statistical measurements through explainable ML models.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBreast cancer is a major public health concern, and early diagnosis and classification are critical for effective treatment. Machine learning and deep learning techniques have shown great promise in the classification and diagnosis of breast cancer. PURPOSE: In this review, we examine studies that have used these techniques for breast cancer classification and diagnosis, focusing on five groups of medical images: mammography, ultrasound, MRI, histology, and thermography. We discuss the use of five popular machine learning techniques, including Nearest Neighbor, SVM, Naive Bayesian Network, DT, and ANN, as well as deep learning architectures and convolutional neural networks.
conclusionOur review finds that machine learning and deep learning techniques have achieved high accuracy rates in breast cancer classification and diagnosis across various medical imaging modalities. Furthermore, these techniques have the potential to improve clinical decision-making and ultimately lead to better patient outcomes.
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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 Socratic graph.