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Machine Learning Based Breast Cancer Detection

Author

Listed:
  • Milan Patel

    (Computer, D.N.Patel College Of Engineering)

  • Ashphak Khan

    (Computer, D.N.Patel College Of Engineering)

  • Tejas Patel

    (Computer, D.N.Patel College Of Engineering)

  • Nikhil Patel

    (Computer, D.N.Patel College Of Engineering)

  • Hiren Chaudhari

    (Computer, D.N.Patel College Of Engineering)

Abstract

In this study, a novel ultra-wideband (UWB) radar-based method for early, non-invasive breast cancer diagnosis is presented. The system uses a curved seventh derivative Gaussian UWB pulse that is filtered by a sharp transition bandpass FIR filter in both monostatic and bistatic radar configurations. The pulse shape enhances radiation power, spectrum efficiency, and FCC-compliant safety. Tumors are located and detected using Specific Absorption Rate (SAR) measurement and backscattered signal processing. Results from experiments and simulations confirm that the system can accurately identify cancers.

Suggested Citation

  • Milan Patel & Ashphak Khan & Tejas Patel & Nikhil Patel & Hiren Chaudhari, 2025. "Machine Learning Based Breast Cancer Detection," International Journal of Latest Technology in Engineering, Management & Applied Science, International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS), vol. 14(5), pages 1114-1118, May.
    • Handle: RePEc:bjb:journl:v:14:y:2025:i:5:p:1114-1118
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