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Related Experiment Video

Updated: Jul 9, 2026

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:05

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

ProCDNet: prostate cancer detection network using quantum machine learning with enhanced addax optimization.

M R Prathap1, K S Vairavel2, C Kumar3

  • 1Electronics and Instrumentation Engineering, Bannari Amman Institute of Technology, Sathyamangalam, India. mrprathap143@gmail.com.

Scientific Reports
|July 7, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces the Prostate Cancer Detection Network (ProCDNet), an advanced model for detecting prostate cancer. ProCDNet achieves high accuracy using Lightweight Deep Residual DenseNet (LDR-DenseNet) and Quantum Machine Learning (QML) for efficient and precise disease classification.

Area of Science:

  • Oncology
  • Artificial Intelligence
  • Quantum Computing

Background:

  • Prostate cancer is a leading global malignancy.
  • Advanced detection models are crucial for effective treatment.
  • Current methods require enhanced efficiency and accuracy.

Purpose of the Study:

  • To develop a novel framework for accurate prostate cancer detection.
  • To integrate feature extraction and disease classification for improved performance.
  • To present the Prostate Cancer Detection Network (ProCDNet).

Main Methods:

  • Utilized Lightweight Deep Residual DenseNet (LDR-DenseNet) for efficient feature extraction.
  • Integrated DenseNet and Residual Learning (ResNet) to enhance feature propagation.
  • Employed Quantum Machine Learning (QML) with Enhanced Addax Optimization (EAO) for classification.
Keywords:
Chebyshev mappingEnhanced addax optimizationProstate cancerQuantum machine learningResidual DenseNet

Related Experiment Videos

Last Updated: Jul 9, 2026

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:05

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

Main Results:

  • ProCDNet achieved high performance metrics: 99.27% Accuracy, 99.08% Precision, 98.28% Recall, 98.85% F1-Score, and 99.96% Specificity.
  • Mean Squared Error (MSE) was recorded at 0.00913.
  • The EAO algorithm ensured robust convergence and improved classification.

Conclusions:

  • ProCDNet offers a highly efficient and accurate solution for prostate cancer detection.
  • The integration of LDR-DenseNet and EAO-QML significantly enhances detection capabilities.
  • This framework represents a significant advancement in oncological diagnostics.