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Cervical cancer classification using a novel hybrid approach.

Jheelam Mondal1, Rajdeep Chatterjee1, Mahendra Kumar Gourisaria1

  • 1School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar, Odisha, India.

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Summary

A new deep learning model, CASPNet, accurately classifies cervical cells as healthy or malignant. This Contextual Attention and Spatial Pooling Network achieves 97.07% accuracy, improving early cancer detection.

Keywords:
cervical cancerclassification of imageshybrid modelmedical image analysisvision transformer

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Area of Science:

  • Medical imaging analysis
  • Computational pathology
  • Artificial intelligence in healthcare

Background:

  • Cervical cancer is a leading global malignancy in women.
  • Early detection via Pap smear analysis is crucial but labor-intensive and error-prone.
  • Automated cell classification can enhance diagnostic accuracy and efficiency.

Purpose of the Study:

  • To develop and evaluate a novel deep learning model, CASPNet, for automated classification of cervical cells.
  • To distinguish between healthy and malignant cervical cells using advanced AI techniques.
  • To improve the accuracy and efficiency of cervical cancer screening.

Main Methods:

  • Proposed CASPNet model integrating multi-head self-attention, Cross-Stage Partial (CSP) network, and Spatial Pyramid Pooling Fast (SPPF) layers.
  • Utilized self-attention for global contextual information and CSP blocks for efficient local feature extraction.
  • Employed SPPF for multi-scale feature extraction to handle varying cell sizes.

Main Results:

  • CASPNet achieved a high accuracy of 97.07% on the SIPAKMED dataset.
  • The model demonstrated superior test accuracy compared to traditional Convolutional Neural Network (CNN) models.
  • Achieved optimal performance with comparable computational efficiency.

Conclusions:

  • CASPNet effectively classifies cervical cells, offering a more precise and reliable diagnostic tool.
  • The integration of self-attention, CSP, and SPPF enhances the model's ability to capture global and local features.
  • This AI-driven approach shows promise for improving cervical cancer screening and early detection.