ArticleScientific reports2025
Detection of breast cancer using machine learning and explainable artificial intelligence.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Machine Learning and Explainable AI for Breast Cancer Patient Prioritization: An Intelligent Decision-Support Framework.Bioengineering (Basel, Switzerland) · 2026Article
- Explainable AI-Derived Spatial Pathological Features of Tumor, Necrosis, and Lymphocytes Identify Key Histological Signatures for Residual Cancer Burden Assessment in Breast Cancer.Diagnostics (Basel, Switzerland) · 2026Article
- Interpretable machine learning model for predicting operative difficulty in robotic total mesorectal excision for mid-low rectal cancer.Journal of robotic surgery · 2026Article
- Review
- FT-MDNNMDs: early detection of breast cancer using fine-tuned multi-deep neural networks with TCGA and clinical image datasets.Scientific reports · 2026Article
- Optimized KNN with domain-informed features and LIME explainability for improved breast cancer classification.BMC medical informatics and decision making · 2026Article
- A panel of machine learning approaches for diagnostic model development and validation in ovarian cancer.Translational cancer research · 2026Article
- Explainable AI in breast cancer ultrasound imaging: current developments and challenges.Frontiers in digital health · 2026Review
- Core-shell structured nanomaterials in dual-modal magnetic resonance imaging guided antitumor effect via combined treatment.Frontiers in chemistry · 2026Article
- Oxidative stress and antioxidants in breast cancer: a double-edged sword.Frontiers in oncology · 2026Review
- HMC-net: a ResNet fused hierarchical multi-scale cross-attention architecture for mammographic breast malignancy recognition incorporating explainable AI.Frontiers in oncology · 2026Article
- Cerium-based nanozymes for chemodynamic therapy: tumor microenvironment-responsive mechanisms and applications.Frontiers in chemistry · 2026Review
- Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery.Current oncology (Toronto, Ont.) · 2025Review
- The Underlying Mechanisms and Emerging Strategies to Overcome Resistance in Breast Cancer.Cancers · 2025Review
- A robust stacked neural network approach for early and accurate breast cancer diagnosis.Frontiers in medicine · 2025Article
- Explainable and uncertainty-aware ensemble framework with causal analysis for breast cancer detection.Frontiers in oncology · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is characterized by the proliferation of abnormal breast cells that eventually turn into malignant tumors. These cancer cells can metastasize to be life-threatening and fatal. An intricate mix of environmental factors and individual genetic composition can lead to the formation of this deadly carcinoma. Improvements in the diagnosis and treatment of cancer are essential given the rising incidence of breast cancer. Over the past few decades, machine learning has helped provide accurate medical diagnosis results. Therefore, this study used diagnostic characteristics of patients and multiple machine learning classifiers to identify breast cancer. Incorporating explainable artificial intelligence techniques revealed the underlying factors for the model predictions, adding a layer of transparency and interpretability. Out of the algorithms, random forest showed the best result, an F1-score of 84%. The stacked ensemble model, which combines the strengths of different models, obtained an F1-score performance of 83%. The research emphasized the results obtained by explainers such as SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), ELI5 (Explain Like I'm Five), Anchor and QLattice (Quantum Lattice) to decipher the findings. Interpretable algorithms can be applied in the medical sector to assist practitioners in predicting breast cancer, reducing diagnostic errors, and improving clinical decision-making.
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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.