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Updated: Jun 25, 2025

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Enhanced cervical precancerous lesions detection and classification using Archimedes Optimization Algorithm with

Ayed S Allogmani1, Roushdy M Mohamed2, Nasser M Al-Shibly3

  • 1University of Jeddah, College of Science and Arts at Khulis, Department of Biology, Jeddah, Saudi Arabia.

Scientific Reports
|May 27, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an AI algorithm for early cervical cancer detection using medical images. The novel CPLDC-AOATL method achieves 99.53% accuracy, improving diagnostic capabilities for women.

Keywords:
Archimedes Optimization AlgorithmCervical cancerHuman papillomavirusMedical imageTransfer learning

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Cervical cancer (CC) is a major global health concern for women, often linked to Human Papillomavirus (HPV) infection.
  • Early detection and treatment significantly improve survival rates, but access to screening is limited in low-resource settings.
  • Deep learning (DL) offers promising advancements for automated, sensitive, and rapid CC screening.

Purpose of the Study:

  • To design and evaluate an enhanced algorithm for detecting and classifying cervical precancerous lesions (CPLs) using medical images.
  • To improve the accuracy and efficiency of cervical cancer diagnosis through advanced computational techniques.

Main Methods:

  • The study proposes the CPLDC-AOATL algorithm, incorporating bilateral filtering (BF) for noise reduction in medical images.
  • Feature extraction is performed using the Inception-ResNetv2 model, with hyperparameters optimized by the Archimedes Optimization Algorithm (AOA).
  • A bidirectional long short-term memory (BiLSTM) model is employed for the final cancer detection process.

Main Results:

  • The CPLDC-AOATL algorithm demonstrated a high diagnostic accuracy of 99.53% on a benchmark dataset.
  • The proposed method outperformed existing approaches in detecting and classifying cervical precancerous lesions.
  • The integration of BF, Inception-ResNetv2, AOA, and BiLSTM proved effective for CC diagnosis.

Conclusions:

  • The CPLDC-AOATL algorithm presents a highly accurate and effective AI-driven solution for cervical cancer detection.
  • This approach holds potential for improving early diagnosis, especially in resource-limited regions.
  • The study highlights the efficacy of transfer learning and optimization algorithms in medical image analysis for oncology.