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Updated: Jan 18, 2026

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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Deep dive into deep learning methods for cervical cancer detection and classification.

Pooja Patre1, Dipti Verma2

  • 1Computer Science and Engineering, Vishwavidyalaya Engineering College Ambikapur, Chhattisgarh, Ambikapur, India.

Reports of Practical Oncology and Radiotherapy : Journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology
|September 8, 2025
PubMed
Summary
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Deep learning shows promise for improving cervical cancer diagnosis. This review analyzes deep learning methods, evaluation metrics, and challenges for early detection and treatment.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Cervical cancer remains a major global health concern, necessitating advanced diagnostic tools.
  • Deep learning (DL) offers significant potential for enhancing cervical cancer detection and classification accuracy.

Purpose of the Study:

  • To conduct a comprehensive review of deep learning methodologies applied to cervical cancer diagnosis.
  • To analyze critical approaches, evaluation metrics, and persistent challenges in the field.
  • To guide future research and clinical integration of DL for improved patient outcomes.

Main Methods:

  • Exploration of various deep learning architectures, focusing on Convolutional Neural Networks (CNNs).
  • Application of DL models for segmentation and classification of cervical cytology images.
Keywords:
cervical cancerdeep learningmachine learningsegmentation

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  • Review of key performance indicators including accuracy, sensitivity, specificity, and Area Under the Curve (AUC).

    • Main Results:

    • Deep learning models, particularly CNNs, demonstrate potential in analyzing cervical cytology images.
    • Evaluation metrics like accuracy, sensitivity, specificity, and AUC are crucial for assessing model performance.
    • Significant challenges persist, including limited annotated datasets and the need for model robustness.

    Conclusions:

    • Advancements in DL offer promising avenues for early cervical cancer detection and treatment.
    • Strategies such as data augmentation, transfer learning, and semi-supervised learning can address current limitations.
    • Further research and clinical implementation are essential to leverage DL for combating cervical cancer.