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Related Experiment Video

Updated: May 9, 2025

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

Enhancing Breast Cancer Detection Through Optimized Thermal Image Analysis Using PRMS-Net Deep Learning Approach.

Mudassir Khan^1,2, Mazliham Mohd Su'ud³, Muhammad Mansoor Alam^2,4

¹Department of Computer Science, College of Computer Science, Applied College Tanumah, King Khalid University, P.O. Box: 960, 61421, Abha, Saudi Arabia.

Journal of Imaging Informatics in Medicine

|May 6, 2025

Summary

This summary is machine-generated.

A novel deep learning model, Progressive Residual Multi-Class Support Vector Machine-Net (PRMS-Net), achieves 99.63% accuracy for early breast cancer detection. This advanced tool aids radiologists in improving diagnostic accuracy and patient outcomes.

Keywords:

Breast cancer Diagnostic accuracy Early detection Feature extraction Fivefold cross-validation Image classification Machine learning Medical imaging PRMS-Net Progressive residual networks ResNet-50

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Last Updated: May 9, 2025

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

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Published on: May 5, 2011

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Published on: August 30, 2013

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:

Oncology
Medical Imaging
Artificial Intelligence

Background:

Breast cancer is a leading cause of mortality in women globally.
Early detection is crucial for effective therapy and improved survival rates.
Current diagnostic methods require enhancement for accuracy and efficiency.

Purpose of the Study:

To develop and evaluate an advanced deep learning model for early breast cancer detection.
To improve diagnostic accuracy and reduce false positives/negatives in breast cancer assessment.
To enhance the capabilities of radiologists in identifying breast cancer at its earliest stages.

Main Methods:

Integration of Progressive Residual Networks (PRN) and ResNet-50 within a Progressive Residual Multi-Class Support Vector Machine-Net (PRMS-Net) framework.
Utilizing deep learning for optimized feature extraction and classification.
Employing a fivefold cross-validation approach to assess model reliability and generalizability.

Main Results:

The PRMS-Net model achieved a high accuracy of 99.63% in tests.
Demonstrated strong performance in precision, recall, and F1 scores, indicating high sensitivity and specificity.
Error distribution analysis validated the model's practical applicability in medical image processing.

Conclusions:

PRMS-Net serves as a reliable and efficient tool for early breast cancer detection.
The model aids radiologists in enhancing diagnostic accuracy and reducing misclassifications.
PRMS-Net has the potential to significantly improve patient prognosis through early intervention.