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Updated: May 1, 2026

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

Trustworthy Deep Feature Extraction and Ensemble-Based Machine Learning Approach for Breast Cancer Detections.

Md Rashed¹, Mohammad Kamrul Hasan², Md Imran Hossain¹

¹Department of Information and Communication Engineering Pabna University of Science and Technology Pabna Bangladesh.

Healthcare Technology Letters

|April 30, 2026

Summary

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This summary is machine-generated.

This study introduces a novel deep learning and machine learning approach for accurate breast cancer detection. The combined strategy achieves 97.50% accuracy, improving patient care and outcomes.

Area of Science:

Oncology
Medical Imaging
Artificial Intelligence in Medicine

Background:

Breast cancer (BC) is a leading cause of cancer-related deaths globally.
Current BC detection methods face challenges in combining high accuracy with interpretability, especially with complex imaging data.
Diagnostic accuracy can be subjective and influenced by the clinician's expertise.

Purpose of the Study:

To develop a reliable breast cancer detection strategy by integrating deep learning (DL) and ensemble machine learning (ML) techniques.
To enhance the accuracy and interpretability of breast cancer diagnosis from medical images.
To improve clinical decision-making and patient outcomes in breast cancer care.

Main Methods:

Utilized a pre-trained deep learning model for effective feature extraction from breast cancer images.

Keywords:

SVM VGG‐16 breast cancer deep learning ensemble learning machine learning

Related Experiment Videos

Last Updated: May 1, 2026

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

Applied eight different machine learning models for breast cancer identification.

Evaluated model performance using precision, recall, F1-score, confusion matrices, and ROC curves.

Main Results:

Achieved a high accuracy rate of 97.50%, with precision at 97.15%, recall at 97.00%, and F1-score at 96.98%.
Demonstrated superior performance compared to existing state-of-the-art breast cancer detection models.
Identified the support vector classifier, when combined with the pre-trained VGG-16 architecture, as the most effective ML model.

Conclusions:

The proposed hybrid DL-ML strategy offers a significant advancement in breast cancer detection.
The approach provides a reliable and accurate method for identifying breast cancer, aiding clinical decisions.
This research contributes to improved patient care and better breast cancer outcomes through enhanced diagnostic tools.