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Quadratic Models

Quadratic Models

Quadratic models are mathematical representations used to describe relationships in which the rate of change changes at a constant rate. These models appear in a wide variety of natural and engineered systems, especially those involving motion, forces, and optimization. One common application is analyzing the vertical motion of objects influenced by gravity, such as a ball thrown into the air.In such scenarios, the object's height changes over time in a curved pattern, rising to a maximum point...

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Updated: Jun 27, 2026

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

Machine Learning Approach to Quadratic Programming-Based Microwave Imaging for Breast Cancer Detection.

Sandra Costanzo^1,2,3,4, Alexandra Flores¹, Giovanni Buonanno¹

¹Dipartimento di Ingegneria Informatica, Modellistica, Elettronica e Sistemistica, Università della Calabria, 87036 Rende, Italy.

Sensors (Basel, Switzerland)

|June 10, 2022

Summary

This summary is machine-generated.

This study introduces a new microwave imaging technique combining the Born iterative method and convolutional neural networks for breast cancer detection. The method significantly reduces reconstruction time while maintaining over 90% accuracy in identifying breast phantom permittivity.

Keywords:

Born iterative method breast phantoms convolution neural network inverse scattering permittivity strong dielectric scatterers

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Area of Science:

Medical Imaging
Computational Electromagnetics
Artificial Intelligence in Healthcare

Background:

Microwave imaging offers a non-ionizing approach for breast cancer detection.
Solving the inverse scattering problem in microwave imaging is computationally intensive and ill-posed.
Accurate reconstruction of dielectric properties is crucial for differentiating cancerous tissue.

Purpose of the Study:

To develop a novel, accelerated technique for microwave imaging reconstruction.
To accurately recover the dielectric permittivity of breast phantoms from scattering data.
To enhance the clinical applicability of microwave imaging for breast cancer detection.

Main Methods:

Integration of the Born iterative method (BIM) with a quadratic programming (QP) approach.
Application of convolutional neural networks (CNNs) to accelerate the inverse problem solution.
Validation using simulated breast phantoms in a circular imaging configuration.

Main Results:

The combined BIM-CNN approach significantly reduces image reconstruction time.
High accuracy (exceeding 90%) was achieved in recovering breast phantom permittivity.
The method demonstrates robust performance across various validation scenarios.

Conclusions:

The proposed BIM-CNN technique offers a fast and accurate solution for microwave imaging reconstruction.
CNNs can substantially decrease the computational burden in microwave breast cancer detection.
This advancement holds promise for improving the efficiency and accuracy of clinical microwave imaging systems.