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Equilibrium Optimization Algorithm with Ensemble Learning Based Cervical Precancerous Lesion Classification Model.

Rasha A Mansouri1, Mahmoud Ragab2,3

  • 1Department of Biochemistry, Faculty of Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

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Summary
This summary is machine-generated.

This study introduces an AI technique for classifying cervical precancerous lesions using colposcopy images. The novel approach enhances cervical cancer screening accuracy by integrating deep learning models for improved early detection.

Keywords:
cervical cancerdecision makingensemble learninghealthcaremedical imaging

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Artificial intelligence (AI), deep learning (DL), and machine learning (ML) are increasingly utilized in biomedical fields for automated diagnosis and prognosis.
  • Cervical cell (CCL) classification is vital for early cervical cancer (CC) screening, traditionally relying on manual feature engineering.
  • Convolutional Neural Networks (CNNs) learn features automatically but may overlook latent image correlations, impacting classification accuracy.

Purpose of the Study:

  • To develop an advanced AI technique for classifying cervical precancerous lesions from colposcopy images.
  • To improve the accuracy and efficiency of cervical cancer screening through automated image analysis.
  • To address limitations in traditional methods and CNNs by incorporating ensemble learning and optimized feature extraction.

Main Methods:

  • The study presents an Equilibrium Optimizer with Ensemble Learning-based Cervical Precancercous Lesion Classification on Colposcopy Images (EOEL-PCLCCI) technique.
  • DenseNet-264 architecture serves as the feature extractor, with the Equilibrium Optimizer (EO) algorithm optimizing hyperparameters.
  • An ensemble of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) classifiers, using weighted voting, performs the final classification.

Main Results:

  • The EOEL-PCLCCI technique demonstrated superior performance in identifying and classifying cervical cancer on colposcopy images.
  • Simulation analysis on a benchmark dataset confirmed the effectiveness of the proposed algorithm.
  • The method showed significant improvements over existing deep learning models in classifying cervical precancerous lesions.

Conclusions:

  • The developed EOEL-PCLCCI technique offers a promising advancement in automated cervical cancer screening.
  • The integration of EO for hyperparameter optimization and ensemble learning enhances classification accuracy.
  • This AI-driven approach has the potential to improve early detection rates and patient outcomes in cervical cancer diagnosis.