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Using Deep Neural Network Approach for Multiple-Class Assessment of Digital Mammography.

Shih-Yen Hsu1, Chi-Yuan Wang2, Yi-Kai Kao3

  • 1Department of Information Engineering, I-Shou University, Kaohsiung City 84001, Taiwan.

Healthcare (Basel, Switzerland)
|December 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a deep neural network for classifying mammography images, achieving 86.37% accuracy. The fully convolutional dense connection network (FC-DCN) model offers a promising solution for consistent breast cancer detection.

Keywords:
classificationdeep neural networkmammography

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Oncology

Background:

  • Breast cancer affects over ten thousand women annually in Taiwan.
  • Mammography is a key diagnostic tool but suffers from operator variability and subjective interpretation, leading to inconsistent results.
  • There is a need for objective and reliable methods for mammography image analysis.

Purpose of the Study:

  • To develop and evaluate a deep neural network algorithm for classifying mammography images.
  • To improve the consistency and accuracy of breast cancer detection using artificial intelligence.
  • To explore the effectiveness of a fully convolutional dense connection network (FC-DCN) for this task.

Main Methods:

  • A retrospective study collected clinical mammography images.
  • Images were classified using the Breast Imaging Reporting and Data-Analyzing System (BI-RADS).
  • A fully convolutional dense connection network (FC-DCN) was employed, utilizing image preprocessing, data augmentation, and transfer learning.

Main Results:

  • The developed model achieved an accuracy of 86.37%.
  • Sensitivity was recorded at 100%, and specificity at 72.73%.
  • The FC-DCN framework effectively reduced training parameters while yielding a reasonable classification model.

Conclusions:

  • The deep neural network, based on the FC-DCN model, demonstrates significant potential for accurate mammography image classification.
  • This AI-driven approach can help overcome the limitations of traditional mammography interpretation.
  • The study highlights the feasibility of using advanced deep learning techniques for improving breast cancer diagnosis.