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Related Experiment Video

Updated: Aug 3, 2025

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

BC²NetRF: Breast Cancer Classification from Mammogram Images Using Enhanced Deep Learning Features and

Kiran Jabeen¹, Muhammad Attique Khan¹, Jamel Balili^2,3

¹Department of Computer Science, HITEC University, Taxila 47080, Pakistan.

Diagnostics (Basel, Switzerland)

|April 13, 2023

Summary

This summary is machine-generated.

This study introduces an automated framework for breast cancer classification from mammograms. The novel approach enhances image contrast and uses deep learning for improved early diagnosis accuracy, outperforming existing methods.

Keywords:

augmentation breast cancer contrast enhancement deep learning feature fusion feature optimization mammogram images neural networks

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Last Updated: Aug 3, 2025

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Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Breast cancer is a leading cause of death in women, necessitating accurate and early diagnosis.
Manual mammogram analysis is challenging due to image complexities and requires expert interpretation.
Existing AI methods face limitations in distinguishing cancerous regions and model training.

Purpose of the Study:

To develop an automated computerized framework for enhanced breast cancer classification using mammogram images.
To improve diagnostic accuracy and overcome challenges in current AI-based breast cancer detection.
To provide a robust and reliable tool for early breast cancer identification.

Main Methods:

A novel haze-reduced local-global technique was used for image contrast enhancement.
Dataset augmentation was performed on enhanced images to improve deep learning model training.
A fine-tuned EfficientNet-b0 model was trained using deep transfer learning, followed by feature extraction, fusion, and selection using Equilibrium-Jaya and Regula Falsi.
Machine learning classifiers were used for the final classification.

Main Results:

The proposed framework achieved high average accuracies of 95.4% on the CBIS-DDSM dataset and 99.7% on the INbreast dataset.
The automated system demonstrated improved accuracy compared to state-of-the-art (SOTA) technologies.
Confidence interval analysis confirmed the consistent performance of the developed framework.

Conclusions:

The developed automated framework offers a significant advancement in breast cancer classification accuracy.
The combination of image enhancement, deep transfer learning, and advanced feature optimization leads to superior diagnostic performance.
This framework holds potential for improving early detection rates and reducing breast cancer mortality.