Predicting Mammographic Breast Density Assessment Using Artificial Neural Networks

Document Type : Original Paper

Authors

1 Department physic, faculty of sciences Rabat, University mohamed V

2 Hassan First University of Settat, High Institute of Health Sciences, Laboratory of Sciences and Health Technologies, Settat, Morocco

3 Departement of Physics, Laboratory of High Energy Physics, Modelling and Simulation, Faculty of Science, Mohammed V Agdal University, Rabat, Kingdom of Morocco

10.22038/ijmp.2023.68587.2202

Abstract

Introduction

Mammographic density is an important risk factor for breast cancer. The assessment of breast density has been considered a very important part of the process of breast diagnosis.

Materials and methods

The application of artificial intelligence in medicine allows analyzing data extracted from the medical reports within the routine clinical evaluation, identifying patterns of treatment patients, and developing interventions for patients.

This study used patient data and predicted patient density in mammogram exams, based on five parameters measured by Volpara software. Next, a model based on the Multi-Layer Perceptron Neural Network was trained to predict patient density in a BIRADS category.

Results

The artificial neural network diagram was identified by the corresponding area under the ROC curve. The model predicted the density of patients with a correct classification rate of 98.2%.

conclusion

The object of this study was to determine the effectiveness of artificial neural networks in predicting patient breast density, based on data collected from patient data DICOM in a University hospital.

Keywords

Main Subjects

Iranian Journal of Medical Physics

Articles in Press, Accepted Manuscript
Available Online from 20 February 2023

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History

  • Receive Date: 22 October 2022
  • Revise Date: 18 February 2023
  • Accept Date: 20 February 2023
  • First Publish Date: 20 February 2023

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