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Related Experiment Video

Updated: Oct 10, 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

An Automatic Detection and Localization of Mammographic Microcalcifications ROI with Multi-Scale Features Using the

Tariq Mahmood^1,2, Jianqiang Li^1,3, Yan Pei⁴

¹The School of Software Engineering, Beijing University of Technology, Beijing 100024, China.

|December 10, 2021

Summary

This summary is machine-generated.

This study introduces an advanced machine learning method for detecting early breast cancer signs in mammograms. The radiomics approach accurately distinguishes between benign and malignant microcalcifications, aiding radiologists in diagnosis.

Keywords:

computer aided diagnosis data-augmentation microcalcification radiomics approach support vector machine

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

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Published on: August 16, 2020

Area of Science:

Medical Imaging
Machine Learning
Radiomics

Background:

Microcalcifications in breast tissue are critical early indicators of breast cancer.
Effective screening relies on accurate detection and classification of these microcalcifications.

Purpose of the Study:

To develop a radiomics-based machine learning model for diagnosing pathological microcalcifications in mammograms.
To provide a decision support system for radiologists in patient diagnosis.

Main Methods:

An adaptive enhancement method using contourlet transform to improve microcalcification visibility.
Extraction of textural and statistical features using wavelet transform and top-hat morphological operator for segmentation.
A radiomic fusion algorithm for classifying features into benign and malignant categories.

Main Results:

The proposed model achieved an area under the curve (AUC) of 0.90, sensitivity of 0.98, and accuracy of 0.98.
Outperformed traditional machine learning models like SVM, KNN, and Random Forest on the MIAS dataset.
Experimental findings align with expert observations, demonstrating high diagnostic performance.

Conclusions:

The developed radiomics approach is effective and practical for early diagnosis of breast microcalcifications.
The model significantly enhances physician work efficiency by providing reliable diagnostic support.
This advanced method holds promise for improving breast cancer screening outcomes.