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Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Related Experiment Video

Updated: Jul 15, 2025

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
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Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

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An Innovative Faster R-CNN-Based Framework for Breast Cancer Detection in MRI.

João Nuno Centeno Raimundo1, João Pedro Pereira Fontes2, Luís Gonzaga Mendes Magalhães2

  • 1Instituto Politécnico de Setúbal, Escola Superior de Tecnologia de Setúbal, 2914-508 Setúbal, Portugal.

Journal of Imaging
|September 27, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an automated breast cancer detection system using Faster R-CNN on MRI scans. The novel preprocessing method enhances accuracy and efficiency in identifying pathological lesions.

Keywords:
breast cancer detectioncomputer visionconvolutional neural networksdeep learningmachine learningmagnetic resonance imaging

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Breast cancer (BC) is the most common cancer globally, with 2.3 million new cases and 685,000 deaths in 2020.
  • Early detection significantly improves treatment effectiveness and patient survival rates.
  • Medical imaging modalities like MRI are crucial for BC detection and diagnosis.

Purpose of the Study:

  • To develop and validate a Faster R-CNN-based framework for automated detection of breast cancer pathological lesions in MRI.
  • To improve the breast MRI preprocessing phase for more robust dataset creation and accurate lesion identification.

Main Methods:

  • A novel Faster R-CNN-based framework was developed for automated BC lesion detection in MRI.
  • An innovative preprocessing method was created to enhance breast MRI slice selection and bounding box annotation.
  • A fully annotated dataset of 922 BC patient MRI cases was used for training and validation.

Main Results:

  • The developed framework achieved a maximum accuracy of 97.83% in detecting pathological lesions.
  • A ten-fold cross-validation resulted in a mean accuracy of 94.46% with a standard deviation of 2.43%.
  • The preprocessing method improved dataset robustness, reduced computation time, and enhanced lesion localization accuracy.

Conclusions:

  • The proposed Faster R-CNN framework with enhanced preprocessing offers a highly accurate and efficient solution for automated breast cancer detection in MRI.
  • This approach can significantly aid radiologists in clinical decision-making, leading to earlier and more precise diagnoses.
  • The publicly released dataset and validated methodology contribute to advancing deep learning applications in breast cancer imaging.