Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

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

Occupational hazard awareness and safety-related knowledge among EMS students: evidence of a potential gap.

Scientific reports·2026
Same author

Corrigendum to "LLM predicts human behavior: A BERT-based approach for conscientiousness personality trait detection from online content" [Acta Psychologica 266 (2026), 106832].

Acta psychologica·2026
Same author

Evaluation of Video-Based Instruction and a 360° Virtual Reality Module on Personal Protective Equipment Competency and Infection Prevention in Healthcare Settings: A Quasi-Experimental Study.

Healthcare (Basel, Switzerland)·2026
Same author

Immune and non-immune hydrops fetalis in a Saudi tertiary center: etiologies, antenatal predictors, perinatal outcomes, and one-year survival in a seven-year cohort.

Frontiers in pediatrics·2026
Same author

LLM predicts human behavior: A BERT-based approach for conscientiousness personality trait detection from online content.

Acta psychologica·2026
Same author

Low muscle mass in interstitial lung disease: a systematic review and meta-analysis of prevalence and clinical associations.

BMC pulmonary medicine·2026

Related Experiment Video

Updated: Jul 5, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

Using hybrid pre-trained models for breast cancer detection.

Sameh Zarif1,2, Hatem Abdulkader3, Ibrahim Elaraby4

  • 1Department of Information Technology, Faculty of Computers and Information, Menoufia University, Shebin El-kom, Menoufia, Egypt.

Plos One
|January 22, 2024
PubMed
Summary
This summary is machine-generated.

A new hybrid deep learning model (CNN+EfficientNetV2B3) accurately identifies invasive ductal carcinoma (IDC) in breast histopathology images. This AI tool enhances early breast cancer detection, improving diagnostic accuracy for pathologists.

More Related Videos

Orthotopic Transplantation of Breast Tumors as Preclinical Models for Breast Cancer
07:45

Orthotopic Transplantation of Breast Tumors as Preclinical Models for Breast Cancer

Published on: May 18, 2020

6.0K
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.9K

Related Experiment Videos

Last Updated: Jul 5, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K
Orthotopic Transplantation of Breast Tumors as Preclinical Models for Breast Cancer
07:45

Orthotopic Transplantation of Breast Tumors as Preclinical Models for Breast Cancer

Published on: May 18, 2020

6.0K
A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

1.9K

Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Breast cancer diagnosis relies heavily on manual histopathology image analysis, which is labor-intensive and prone to inter-observer variability.
  • Accurate and timely diagnosis is critical for effective breast cancer treatment and patient outcomes.
  • Automated analysis of whole slide images (WSIs) offers a potential solution to improve efficiency and accuracy.

Purpose of the Study:

  • To develop and evaluate a hybrid deep learning model for the automated classification of invasive ductal carcinoma (IDC) in breast histopathology images.
  • To compare the performance of the proposed model against existing machine learning and deep learning approaches.

Main Methods:

  • A hybrid deep learning model combining Convolutional Neural Networks (CNNs) and EfficientNetV2B3 was developed.
  • The model was trained and validated on whole slide images (WSIs) for the identification of IDC and non-IDC tissues.
  • Performance was evaluated using metrics including accuracy, precision, recall, F1-score, MCC, AUC-ROC, and AUPRC.

Main Results:

  • The proposed CNN+EfficientNetV2B3 model achieved high performance: 96.3% accuracy, 93.4% precision, 86.4% recall, 89.7% F1-score, 87.6% MCC, 97.5% AUC-ROC, and 96.8% AUPRC.
  • The model significantly outperformed other tested deep learning models, including MobileNet+DenseNet121 and MobileNetV2+EfficientNetV2B0.
  • The results indicate the model's robustness and superiority in classifying breast cancer tissues.

Conclusions:

  • The hybrid CNN+EfficientNetV2B3 model demonstrates superior performance for automated breast cancer detection in histopathology images.
  • This AI-driven approach can serve as a valuable tool to assist pathologists, enhancing diagnostic accuracy and efficiency.
  • The findings suggest a promising advancement in computational pathology for breast cancer diagnosis.