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

Deep learning model for hair artifact removal and Mpox skin lesion analysis and detection.

Scientific reports·2025
Same author

Super learner model for classifying leukemia through gene expression monitoring.

Discover oncology·2024
Same author

Transfer learned deep feature based crack detection using support vector machine: a comparative study.

Scientific reports·2024
Same author

En-DeNet Based Segmentation and Gradational Modular Network Classification for Liver Cancer Diagnosis.

Biomedicines·2023
Same author

Multi-Process Remora Enhanced Hyperparameters of Convolutional Neural Network for Lung Cancer Prediction.

Biomedicines·2023
Same author

Detection of Liver Tumour Using Deep Learning Based Segmentation with Coot Extreme Learning Model.

Biomedicines·2023
Same journal

Development of a Unified Cardiovascular-Pupillary Model for Interpreting Pupil Size Variability as an Autonomic Marker.

Biomedical engineering and computational biology·2026
Same journal

Fractional Soliton Dynamics in Coupled Myelinated Fibers: Comparative Modeling With Beta, Caputo, and Atangana-Baleanu Derivatives.

Biomedical engineering and computational biology·2026
Same journal

Accuracy and Functional Performance of Artificial Intelligence-Based Automated Crown Design Systems: A Systematic Review and Meta-Analysis.

Biomedical engineering and computational biology·2026
Same journal

Scalable HMO-CNN-SVM Framework for Skin Lesion Classification: A Metaheuristic-Driven Approach With Parallelizable Optimization for Cluster Deployment.

Biomedical engineering and computational biology·2026
Same journal

Computational Screening of Microbial Metabolites as Erythropoietin (EPO) Mimetics for the Treatment of Anemia.

Biomedical engineering and computational biology·2026
Same journal

Mechanistic Elucidation of Liujun Jiaoxian Tang in Management of Sepsis Through Metabolomics and Network Pharmacology.

Biomedical engineering and computational biology·2026
See all related articles

Related Experiment Video

Updated: Jun 8, 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

6.7K

Breast Cancer Diagnosis Using Virtualization and Extreme Learning Algorithm Based on Deep Feed Forward Networks.

G Siva Shankar1, Edeh Michael Onyema2,3, Balasubramanian Prabhu Kavin4

  • 1Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur Campus, Chengalpattu, Tamil Nadu, India.

Biomedical Engineering and Computational Biology
|November 4, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an advanced cloud-based machine learning approach for early breast cancer detection. The novel method achieves high accuracy, aiding remote diagnostics and improving patient outcomes.

Keywords:
Remote diagnosticsameliorate crow forage-ELMdeep residual based multiclass for feature extraction approachsmart window vestige deletion

More Related Videos

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.2K
Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis
06:03

Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis

Published on: February 6, 2020

6.6K

Related Experiment Videos

Last Updated: Jun 8, 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

6.7K
Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.2K
Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis
06:03

Integrating Augmented Reality Tools in Breast Cancer Related Lymphedema Prognostication and Diagnosis

Published on: February 6, 2020

6.6K

Area of Science:

  • Medical Imaging and Diagnostics
  • Artificial Intelligence in Healthcare
  • Computational Biology

Background:

  • Breast cancer is a leading cause of death for women globally, necessitating improved early detection and treatment strategies.
  • Cloud computing and machine learning offer solutions for remote diagnostics and telemedicine, particularly in underserved areas.
  • Artificial Neural Networks (ANNs) show promise for disease diagnosis, driving research into advanced computational methods.

Purpose of the Study:

  • To develop and evaluate a novel cloud-based machine learning framework for accurate and efficient breast cancer diagnosis.
  • To enhance early detection capabilities, thereby reducing breast cancer-related mortality.
  • To leverage advanced AI techniques for improved diagnostic accuracy in remote healthcare settings.

Main Methods:

  • A four-stage methodology involving preprocessing, feature extraction, and classification was employed.
  • The Smart Window Vestige Deletion (SWVD) technique, incorporating Savitzky-Golay (S-G) smoothing and adaptive filtering, was used for preprocessing.
  • Deep Residual based Multiclass for architecture (DRMFA) was utilized for feature extraction from histological images, followed by a custom crow forage-ELM (ACF-ELM) for classification.

Main Results:

  • The proposed cloud-based Extreme Learning Machine (ELM) approach demonstrated performance comparable to state-of-the-art technologies.
  • Evaluated on the DDSM and INbreast datasets, the ACF-ELM method outperformed alternative solutions.
  • Achieved high performance metrics: 0.9845 accuracy, 0.96 precision, 0.94 recall, and 0.95 F1 score.

Conclusions:

  • The developed cloud-based machine learning system offers a robust and accurate solution for breast cancer diagnosis.
  • The novel SWVD and ACF-ELM techniques contribute to improved feature extraction and classification in medical imaging.
  • This approach holds significant potential for enhancing telemedicine services and improving breast cancer outcomes, especially in remote areas.