ArticleCancers2024
Ensemble Deep Learning-Based Image Classification for Breast Cancer Subtype and Invasiveness Diagnosis from Whole Slide Image Histopathology.
Article in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Transformer based breast cancer classification from histopathological images using sequential residual recurrent multiscale attention network.Scientific reports · 2026Article
- Article
- A novel multimodal framework integrating pathomics, deep learning, and machine learning for breast cancer histological grades classification.Diagnostic pathology · 2026Article
- Magnification-independent breast cancer diagnosis using a GWO-enhanced vision transformer with multi-stage stain normalization.Scientific reports · 2026Article
- Clinical validation of lightweight CNN architectures for reliable multi-class classification of lung cancer using histopathological imaging techniques.Scientific reports · 2026Article
- Leveraging medical imaging and deep learning for diagnosis of breast cancer using histopathological images.Scientific reports · 2026Article
- Artificial intelligence in the diagnosis of thyroid diseases: applications and challenges.Frontiers in radiology · 2026Review
- Hierarchical Tissue-Specific Modeling of Pathology Images Predicts Response in HER2+ Breast Cancer.Cyborg and bionic systems (Washington, D.C.) · 2026Article
- Breast cancer diagnosis from histopathological images and molecular signatures by fusing features with an explainable AI-based residual tabular network model.Journal of computer-aided molecular design · 2025Article
- A Multimodal Diagnostic Model for Breast Cancer Invasiveness Based on Ultrasound Imaging and Serum Biomarkers.Medicina (Kaunas, Lithuania) · 2025Article
- Evidential deep learning-based ALK-expression screening using H&E-stained histopathological images.NPJ digital medicine · 2025Article
- Predicting ROS1 and ALK fusions in NSCLC from H&E slides with a two-step vision transformer approach.NPJ precision oncology · 2025Article
- The Bayesian mixture expert recognition model for tobacco leaf curing stages based on feature fusion.Plant methods · 2025Article
- Research on the developments of artificial intelligence in radiomics for oncology over the past decade: a bibliometric and visualized analysis.Discover oncology · 2025Article
- Enhanced Multi-Class Breast Cancer Classification from Whole-Slide Histopathology Images Using a Proposed Deep Learning Model.Diagnostics (Basel, Switzerland) · 2025Article
- The Potential Diagnostic Application of Artificial Intelligence in Breast Cancer.Current pharmaceutical design · 2025Review
- Detecting cell types and densities in the tumor microenvironment improves prognostic risk assessment for breast cancer.Biomolecules & biomedicine · 2024Article
- A multi-patch-based deep learning model with VGG19 for breast cancer classifications in the pathology images.Digital healthArticle
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Cancer diagnosis and classification are pivotal for effective patient management and treatment planning. In this study, a comprehensive approach is presented utilizing ensemble deep learning techniques to analyze breast cancer histopathology images. Our datasets were based on two widely employed datasets from different centers for two different tasks: BACH and BreakHis. Within the BACH dataset, a proposed ensemble strategy was employed, incorporating VGG16 and ResNet50 architectures to achieve precise classification of breast cancer histopathology images. Introducing a novel image patching technique to preprocess a high-resolution image facilitated a focused analysis of localized regions of interest. The annotated BACH dataset encompassed 400 WSIs across four distinct classes: Normal, Benign, In Situ Carcinoma, and Invasive Carcinoma. In addition, the proposed ensemble was used on the BreakHis dataset, utilizing VGG16, ResNet34, and ResNet50 models to classify microscopic images into eight distinct categories (four benign and four malignant). For both datasets, a five-fold cross-validation approach was employed for rigorous training and testing. Preliminary experimental results indicated a patch classification accuracy of 95.31% (for the BACH dataset) and WSI image classification accuracy of 98.43% (BreakHis). This research significantly contributes to ongoing endeavors in harnessing artificial intelligence to advance breast cancer diagnosis, potentially fostering improved patient outcomes and alleviating healthcare burdens.
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