Classification of Breast Masses

Current Status
Not Enrolled
Price
Free With Unlimited Credits
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  • Approver: AHRA®
  • ARRT®: Accepted
  • Expiration Date: 6/30/25
  • Credit Hours: 1.25 Category A Credits

Course Acceptance List

  • ARRT: Accepted
  • Florida: Cat A / Technical
  • Texas: Direct
  • NMTCB: Accepted
cme courses radiology - Image of human breast cancer

CQR

cme courses radiology - CQR Breast Sonography
cme courses radiology - CQR Mammography
cme courses radiology - CQR Radiation Therapy
cme courses radiology - CQR Registered Radiologist Assistant

Structured Education

cme courses radiology - CQR Breast Sonography
cme courses radiology - CQR Mammography
cme courses radiology - CQR Radiation Therapy
cme courses radiology - CQR Registered Radiologist Assistant

Course Description And Objectives:

Objectives

  • Understand the prevalence and impact of breast cancer, especially its early detection and treatment on patients’ survival rates.
  • Gain knowledge about the limitations of traditional methods for mammogram analysis and the potential benefits of computer-aided diagnosis (CAD) systems in improving the accuracy of breast cancer diagnosis.
  • Learn about the application of convolutional neural network (CNN) and recurrent neural network (RNN) in medical image analysis, with a focus on mammogram classification.

Course Description

This course focuses on the use of deep learning techniques to detect and classify breast cancer, the most common cancer in women. It covers the worldwide public problem of cancer, with breast cancer having the highest incidence among women. The course emphasizes the importance of early detection and treatment, discusses mammography as the most widely used and effective detection method for breast cancer, and the need for computer-aided diagnosis systems. It covers the application of CNN to medical image analysis, the limitations of using the whole mammogram to classify breast cancer, and the proposed TVNN model combining CNN and RNN to improve classification performance. The course uses the DDSM database to verify the TVNN model and obtain good results.

This activity may be available in multiple formats or from different sponsors. A self-learning activity can be completed only once per biennium.  ARRT® CE Requirements

Classification of Breast Masses Course