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Equilibrium Optimization Algorithm with Deep Learning Enabled Prostate Cancer Detection on MRI Images.

Eunmok Yang1, K Shankar2,3, Sachin Kumar4

  • 1Department of Financial Information Security, Kookmin University, Seoul 02707, Republic of Korea.

Biomedicines
|December 23, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep learning approach for early prostate cancer (PrC) detection using MRI and ultrasound images. The Equilibrium Optimization Algorithm with Deep Learning-based Prostate Cancer Detection and Classification (EOADL-PCDC) method shows superior performance in identifying PrC.

Keywords:
cancer diagnosisdeep learningequilibrium optimizermagnetic resonance imagingprostate cancer

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Oncology

Background:

  • Prostate cancer (PrC) diagnosis is crucial for improving patient survival rates.
  • Early detection of PrC enables timely intervention and better treatment outcomes.
  • Current diagnostic methods can be enhanced with advanced computational techniques.

Purpose of the Study:

  • To develop and evaluate a novel, automatic deep learning (DL) approach for detecting and classifying prostate cancer (PrC) from MRI and ultrasound (US) images.
  • To introduce an explainable AI (XAI) component for decision-making transparency in PrC detection.
  • To enhance the efficiency and accuracy of PrC diagnosis through advanced DL techniques.

Main Methods:

  • The study presents the Equilibrium Optimization Algorithm with Deep Learning-based Prostate Cancer Detection and Classification (EOADL-PCDC) technique.
  • The EOADL-PCDC method incorporates image preprocessing, a CapsNet (capsule network) model for feature extraction, and hyperparameter tuning using the Equilibrium Optimization Algorithm (EOA).
  • A stacked bidirectional long short-term memory (SBiLSTM) model is utilized for the final classification of PrC.

Main Results:

  • The EOADL-PCDC algorithm demonstrated superior performance compared to existing methods on a benchmark MRI dataset.
  • The method achieved high accuracy in detecting and classifying prostate cancer.
  • The integration of custom DL layers and EOA-based hyperparameter tuning significantly improved model efficiency.

Conclusions:

  • The developed EOADL-PCDC technique offers a promising automatic and explainable approach for accurate prostate cancer detection and classification.
  • This DL-based method has the potential to significantly improve early diagnosis and patient outcomes in PrC management.
  • The study highlights the effectiveness of combining advanced DL architectures with optimization algorithms for medical image analysis.