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Implementation of Machine Learning Mechanism for Recognising Prostate Cancer through Photoacoustic Signal.

G Ramkumar1, P Bhuvaneswari2, R Radhika3

  • 1Department of Electronics and Communication Engineering, Saveetha School of Engineering SIMATS, Chennai 602 105, Tamil Nadu, India.

Contrast Media & Molecular Imaging
|October 20, 2022
PubMed
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This summary is machine-generated.

Machine learning techniques applied to photoacoustic (PA) spectroscopy data enable accurate prostate cancer diagnosis. This approach analyzes tissue composition, offering a noninvasive method for early detection with over 94% accuracy.

Area of Science:

  • Biomedical Optics
  • Spectroscopy
  • Machine Learning

Background:

  • Photoacoustic (PA) spectroscopy provides rich data for biological tissue analysis.
  • Analyzing extensive PA data for direct tissue assessment is challenging.
  • Data mining offers a solution for complex biological data interpretation.

Purpose of the Study:

  • To implement machine learning (ML) for prostate cancer diagnosis using PA spectrum assessment.
  • To investigate the composition and distribution of collagen, lipids, and haemoglobin in prostate tissues.
  • To evaluate the effectiveness of ML classifiers in distinguishing between normal and cancerous tissues.

Main Methods:

  • PA signals were preprocessed using the Pwelch method.
  • Feature extraction involved hierarchical clustering and correlation assessment.

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  • Supervised classification utilized Support Vector Machine (SVM), Naïve Bayes (NB), decision tree C4.5, and Linear Discriminant Analysis (LDA).

    • Main Results:

    • A stronger correlation between chemical components' ultrasonic power spectra was observed in diseased tissues.
    • This suggests more uniform microstructural dispersion in tumor tissues compared to normal tissues.
    • All four ML classifiers achieved diagnostic accuracy exceeding 94%.

    Conclusions:

    • The developed ML approach demonstrates significant promise for noninvasive, early detection of prostate cancer.
    • The method's accuracy surpasses that of benchmark medical techniques.
    • PA spectroscopy combined with ML offers a powerful tool for cancer diagnostics.