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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

Summary

This summary is machine-generated.

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 Learning Mask R-CNN Prostate Cancer Prostate Segmentation Transfer 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.