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

Related Concept Videos

Cancer Survival Analysis

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

You might also read

Related Articles

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

Sort by

Same author

Bridging modalities: a deep learning framework for brain tumor classification via CT-MRI integration and model fusion.

Frontiers in computational neuroscience·2026

Same author

A hybrid optimized framework with energy shape prior segmentation for brain tumor detection in MRI images.

Digital health·2026

Same author

"Healing the Wound That the Blow Made": A Scoping Review of Anti-Racist Restorative and Reparatory Justice Programs.

Health promotion practice·2026

Same author

Retraction Note: Strengthening network DDOS attack detection in heterogeneous IoT environment with federated XAI learning approach.

Scientific reports·2026

Same author

Integrative multi-stage deep learning framework for ovarian tumor ultrasound classification with explainability and confidence estimation.

Frontiers in medicine·2026

Same author

Quantum transfer learning for cross-domain cybersecurity threat detection and categorization.

Scientific reports·2026

Search research articles

Related Experiment Video

Updated: Aug 18, 2025

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

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

Published on: August 16, 2020

Breast Cancer Detection Using Convoluted Features and Ensemble Machine Learning Algorithm.

Muhammad Umer¹, Mahum Naveed², Fadwa Alrowais³

¹Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan.

|December 11, 2022

Summary

This summary is machine-generated.

This study introduces an ensemble learning model for accurate breast cancer detection. The model achieved 100% accuracy in classifying malignant and benign tumors, improving upon existing methods.

Keywords:

breast cancer prediction deep convoluted features ensemble learning healthcare

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Related Experiment Videos

Last Updated: Aug 18, 2025

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

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

Published on: August 16, 2020

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Area of Science:

Oncology
Machine Learning
Medical Imaging Analysis

Background:

Breast cancer is a significant cause of mortality among women, particularly in developing nations.
Early detection through screening and diagnosis is crucial for effective breast cancer prevention and treatment.
Current diagnostic methods can be enhanced with advanced computational approaches for improved accuracy.

Purpose of the Study:

To develop and evaluate an ensemble learning-based voting classifier for accurate breast cancer detection.
To integrate deep convoluted features from microscopic images with ensemble methods for enhanced classification.
To compare the performance of the proposed framework against state-of-the-art approaches.

Main Methods:

Extraction of deep convoluted features from microscopic breast tissue images.
Implementation of an ensemble voting classifier combining logistic regression and stochastic gradient descent.
Utilizing the extracted features as input for the ensemble classifier to distinguish between malignant and benign tumors.

Main Results:

The proposed ensemble voting classifier achieved a classification accuracy of 100%.
The integration of deep convoluted features significantly improved the accuracy of tumor classification.
The approach demonstrated superior performance compared to existing state-of-the-art methods in breast cancer detection.

Conclusions:

Ensemble learning combined with deep convoluted features offers a highly accurate framework for breast cancer detection.
The proposed method shows significant potential for improving early diagnosis and treatment of breast cancer.
This approach provides an optimized solution for classifying tumors, addressing a critical need in women's health.