Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Mar 15, 2026

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

Ensemble Deep Learning-Based High-Precision Framework for Breast Cancer Detection from Histopathological Images.

Faizan Ahmad¹, Arfan Jaffar¹, Ghazanfar Latif²

¹Department of Computer Science, Superior University, Lahore 547700, Pakistan.

Diagnostics (Basel, Switzerland)

|March 14, 2026

Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Combination of degrading with non-degrading endophytic bacteria synergistically enhances rice resistance to chlorpyrifos stress.

Journal of advanced research·2026

Same author

EEG-based harmful brain activity classification using deep learning and feature fusion.

Scientific reports·2026

Same author

CRISPR Cas9 mediated genome editing to induce apoptosis a new strategy to tackle cancer.

Nucleosides, nucleotides & nucleic acids·2026

Same author

Quantifying Regional and Age-Dependent Microstructural Changes in Porcine Ventricles from Neonatal to Adulthood Using DT-MRI and TPEF-SHG Microscopy.

Annals of biomedical engineering·2026

Same author

Challenges in translating AI-driven ASD/ADHD diagnosis: A methodological systematic review.

International journal of medical informatics·2026

Same author

A robust deep learning approach for impulse noise filtering using hybrid auto-encoder with fuzzy median filter.

PloS one·2026

Same journal

Correction: Luca et al. Global and Regional Diagnostic Results of Progress Toward Cervical Cancer Elimination, According to the WHO Strategy: A Systematic Literature Review with Narrative Synthesis. <i>Diagnostics</i> 2026, <i>16</i>, 1224.

Diagnostics (Basel, Switzerland)·2026

Same journal

Association Between Systemic Inflammatory Response Biomarkers and Disease Activity in Systemic Lupus Erythematosus: A Multi-Center Retrospective Study.

Diagnostics (Basel, Switzerland)·2026

Same journal

Vertebrogenic Low Back Pain and Basivertebral Nerve Ablation: A Review of Mechanisms, Imaging-Driven Selection, and Clinical Outcomes.

Diagnostics (Basel, Switzerland)·2026

Same journal

Multivalvular Carcinoid Heart Disease: The Role of Echocardiography in Diagnosis and Selection for Heterotopic Bicaval Valve Implantation.

Diagnostics (Basel, Switzerland)·2026

Same journal

Data-Efficient and Explainable Multimodal Survival Prediction in NSCLC Using Deep Image Embeddings, Clinical Variables, and Gradient-Boosted Trees.

Diagnostics (Basel, Switzerland)·2026

Same journal

Anomalous Left Coronary Artery from the Pulmonary Artery: Cinematic Volume Rendering Technique for Enhanced Anatomic Visualization.

Diagnostics (Basel, Switzerland)·2026

See all related articles

This summary is machine-generated.

This study introduces a hybrid deep learning framework integrating Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) for enhanced breast cancer diagnosis from histopathological images. The proposed model achieves high accuracy and robustness, offering an interpretable solution for automated detection.

Area of Science:

Medical Imaging
Artificial Intelligence
Computational Pathology

Background:

Histopathological image analysis is standard for breast cancer diagnosis.
Current deep learning and ViT architectures face challenges in capturing complex patterns, leading to overfitting and optimization issues.

Purpose of the Study:

To propose a novel four-phase hybrid framework to enhance feature fusion for improved breast cancer diagnosis.
To increase the strength, robustness, and generalization ability of diagnostic models.

Main Methods:

A patient-wise data split (70-15-15) with augmentation and cross-validation was used.
Independent training of CNNs (VGG16, ResNet50, DenseNet121) and ViTs (DeiT, CaiT, T2T-ViT, Swin Transformer) established baseline performance.
A hybrid framework combined the best CNN and ViT models using self-attention for cross-modal feature fusion, Global Average Pooling, and feature scaling.

Keywords:

breast cancer screening convolutional neural network cross-attention deep learning ensemble learning feature concatenation histopathological images overfitting mitigation precision engineering vision transformers

Related Experiment Videos

Last Updated: Mar 15, 2026

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

A Multimodal Imaging Framework to Advance Phenotyping of Living Label-free Breast Cancer Cells

Published on: August 22, 2025

Main Results:

The hybrid framework achieved 98.7% accuracy and 98.7% F1-score on the BreakHis dataset.
The model demonstrated 95.8% accuracy on the external BACH dataset.
XGBoost was the top-performing machine learning classifier, with results supported by Grad-CAM and Grad-CAM++ interpretability.

Conclusions:

Integrating CNNs and ViTs via self-attention provides a robust and interpretable solution for automated breast cancer diagnosis.
The proposed framework effectively addresses limitations of existing deep learning architectures.