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September 10, 2025Journal of Applied Research and TechnologyOpen Access

Adaptive Archimedes optimization algorithm trained deep learning for polycystic ovary syndrome detection using ultrasound image

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Authors

KSKalpesh Shelke

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Implication

This analysis demonstrates high accuracy in polycystic ovary syndrome detection using deep learning and ultrasound images, suggesting improved diagnosis techniques.

Key Points

  • The deep learning model achieved an accuracy of 90.6% for detecting polycystic ovary syndrome.
  • Sensitivity and specificity values of the model were 91.8% and 92.8% respectively, indicating reliable performance.
  • Feature extraction included statistical features and Speeded-Up Robust Feature for enhanced detection accuracy.
  • The deep Q Net parameters were optimized using the adaptive Archimedes optimization algorithm to improve diagnostic efficiency.

Cite This Study

Kalpesh Shelke (2025) studied this question.

synapsesocial.com/papers/68c193de9b7b07f3a061776ahttps://doi.org/10.22201/icat.24486736e.2025.23.4.2753
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Also Consider

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  5. 5Diagnosis of Polycystic Ovary Syndrome (PCOS) using Deep Learning and Classification Technique’s2024 · 2 citations