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Updated: Dec 25, 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

Deep feature-based automatic classification of mammograms.

Ridhi Arora¹, Prateek Kumar Rai², Balasubramanian Raman³

¹Indian Institute of Technology Roorkee, Roorkee, India. rarora@cs.iitr.ac.in.

Medical & Biological Engineering & Computing

|March 23, 2020

Summary

This summary is machine-generated.

This study introduces a deep learning model for breast cancer classification from mammograms. The computer-aided diagnosis (CADx) system achieved 88% accuracy in distinguishing benign from malignant tumors.

Keywords:

Breast lesion Computer-aided diagnosis Feature extraction Image processing Medical imaging

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Breast cancer is a leading cause of death in women globally.
Early detection is crucial but challenging.
Mammography is key for diagnosis, but manual interpretation is labor-intensive.

Purpose of the Study:

To develop an automated computer-aided diagnosis (CADx) system for breast cancer classification.
To enhance the accuracy and efficiency of mammogram analysis.
To assist radiologists in differentiating benign and malignant tumors.

Main Methods:

Proposed a deep ensemble transfer learning model for automatic feature extraction.
Utilized a neural network classifier (nntraintool) for classification.
Pre-processed mammogram images before inputting into the ensemble model.

Main Results:

Achieved an accuracy of 0.88 for breast cancer classification.
Obtained an Area Under the Curve (AUC) of 0.88.
Demonstrated robust feature extraction and classification capabilities.

Conclusions:

The proposed deep learning methodology is a promising CADx system.
The approach offers a robust solution for automated breast cancer classification.
This system can aid in improving diagnostic outcomes for mammography.