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Related Experiment Video

Updated: Sep 17, 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

Innovative deep learning classifiers for breast cancer detection through hybrid feature extraction techniques.

S Vijayalakshmi¹, Binay Kumar Pandey², Digvijay Pandey³

¹Department of Electronics and Communication Engineering, Sona College of Technology, Salem, 636005, Tamilnadu, India.

Scientific Reports

|July 2, 2025

Summary

This summary is machine-generated.

This study introduces a hybrid deep learning approach for improved breast cancer detection from mammograms. Combining statistical features with a BiLSTM-CNN model achieved 97.14% accuracy, aiding early cancer screening.

Keywords:

Deep learning Gray-level run-length matrix Grey level co-occurrence Grey level difference matrix Machine learning Mammogram images Nakagami distribution parameter

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

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Last Updated: Sep 17, 2025

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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:

Medical Imaging
Artificial Intelligence in Healthcare
Oncology

Background:

Breast cancer is a leading cause of death in women, making early detection crucial for survival.
Accurate mammogram analysis is essential for effective breast cancer screening and diagnosis.

Purpose of the Study:

To develop and evaluate a hybrid classification method for enhanced mammogram analysis.
To combine handcrafted statistical features with deep learning for improved breast cancer detection.

Main Methods:

Mammogram preprocessing using Shearlet Transform.
Image segmentation via Improved Otsu thresholding and Canny edge detection.
Feature extraction using GLCM, GLRLM, and 1st-order statistics, fed into a 2D BiLSTM-CNN model.

Main Results:

The hybrid method achieved 97.14% accuracy on the MIAS dataset.
Outperformed several benchmark models in mammogram classification.
Demonstrated the effectiveness of combining statistical and deep learning features.

Conclusions:

The proposed hybrid approach significantly improves breast cancer classification performance.
This method shows potential to assist radiologists in more effective breast cancer screening.
The integration of handcrafted features and deep learning offers a promising direction for medical image analysis.