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Prostate Segmentation in MRI Images using Transfer Learning based Mask RCNN.

Maryam Shabbir1, Zobia Suhail1, Nida Hafeez2

  • 1Department of Computer Science, University of the Punjab, Lahore, Pakistan.

Current Medical Imaging
|June 14, 2024
PubMed
Summary

This study introduces a deep learning approach for prostate cancer detection. Utilizing transfer learning with Mask R-CNN, it enhances prostate segmentation accuracy for improved diagnostic capabilities.

Keywords:
Deep LearningMask R-CNNProstate CancerProstate SegmentationTransfer Learning.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Oncology

Background:

  • Prostate cancer (PCa) is a leading cause of death in males globally.
  • Deep learning-based Computer Aided Detection (CAD) systems are gaining traction for PCa diagnosis.
  • Existing CAD systems show promising results in segmentation, detection, and classification.

Purpose of the Study:

  • To perform prostate segmentation using a transfer learning-based Mask R-CNN model.
  • To enhance the accuracy of prostate cancer detection through improved segmentation.
  • To contribute to the development of advanced diagnostic tools for PCa.

Main Methods:

  • Application of transfer learning techniques.
  • Implementation of the Mask R-CNN deep learning architecture.
  • Focus on automated prostate segmentation for PCa detection.

Main Results:

  • The study aims to achieve high accuracy in prostate segmentation.
  • The proposed method is expected to be beneficial for prostate cancer detection.
  • Transfer learning-based Mask R-CNN shows potential for improved diagnostic outcomes.

Conclusions:

  • The research discusses current limitations and future prospects in PCa detection.
  • Findings highlight the efficacy of deep learning for medical image analysis.
  • The study contributes to the advancement of automated diagnostic systems in oncology.