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Transfer Learning-Based Multi-Scale Denoising Convolutional Neural Network for Prostate Cancer Detection.

Kwok Tai Chui1, Brij B Gupta2,3,4,5, Hao Ran Chi6

  • 1Department of Electronic Engineering and Computer Science, School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China.

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Summary
This summary is machine-generated.

A deep learning model enhances prostate cancer detection by suppressing noise in medical images. This multi-scale denoising convolutional neural network (MSDCNN) improves diagnostic accuracy, reducing medical personnel workload.

Keywords:
automatic diagnosisconvolutional neural networkdeep learningimage denoisingprostate cancertransfer learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Prostate cancer is a significant global health concern, ranking as the 4th most common cancer.
  • Accurate and efficient diagnosis of prostate cancer is crucial, yet challenging due to image noise.
  • Reducing the workload for medical professionals in prostate cancer diagnosis is a key objective.

Purpose of the Study:

  • To develop a deep learning model for improved prostate cancer detection (PCD).
  • To enhance diagnostic accuracy in noisy medical images.
  • To decrease the diagnostic workload for medical personnel.

Main Methods:

  • A multi-scale denoising convolutional neural network (MSDCNN) was designed for noise suppression in medical images.
  • Transfer learning was employed, utilizing domain knowledge from heterogeneous prostate cancer datasets.
  • Gaussian noise was intentionally introduced to source datasets prior to knowledge transfer.

Main Results:

  • The proposed MSDCNN model demonstrated a significant improvement in accuracy, exceeding existing methods by over 10%.
  • Ablation studies confirmed the individual contributions of denoising (2.80% accuracy increase), multi-scale processing (3.30%), and transfer learning (3.13%).
  • Performance was validated on four benchmark prostate cancer datasets.

Conclusions:

  • The study validates the critical role of image noise suppression in enhancing diagnostic performance.
  • Transferring knowledge from heterogeneous datasets within the same domain significantly benefits model accuracy.
  • The developed MSDCNN model offers a promising solution for accurate and efficient prostate cancer detection.