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Related Experiment Video

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Optimising ovarian tumor classification using a novel CT sequence selection algorithm.

K V Bhuvaneshwari1, Husam Lahza2, B R Sreenivasa3

  • 1Department of Information Science & Engineering, Bapuji Institute of Engineering & Technology, Davanagere, Karnataka, India.

Scientific Reports
|October 24, 2024
PubMed
Summary

This study introduces a novel deep learning approach using a CT Sequence Selection Algorithm to accurately classify ovarian tumors from CT scans. The method shows superior performance in distinguishing malignant from early-stage ovarian cancer, aiding early detection.

Keywords:
CNNCancerComputed tomography (CT) sequenceGynecologicalRadiologistResNet50V2

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Gynaecological cancers, particularly ovarian cancer, pose a significant public health challenge, especially in regions with limited healthcare resources.
  • Late-stage diagnosis due to awareness, pathology, and screening access issues leads to poor patient outcomes.
  • Accurate and early classification of ovarian tumors is crucial for effective treatment and improved survival rates.

Purpose of the Study:

  • To enhance the accuracy of ovarian tumor classification using advanced deep learning techniques.
  • To differentiate between malignant and early-stage ovarian cancers for timely intervention.
  • To develop and validate a novel algorithm for optimizing CT image selection in deep learning models.

Main Methods:

  • Utilized three pre-trained deep learning models: Xception, ResNet50V2, and ResNet50V2FPN.
  • Employed publicly available Computed Tomography (CT) scan data, specifically TIFF images.
  • Developed and integrated a novel CT Sequence Selection Algorithm to optimize image data for classification.

Main Results:

  • The ResNet50V2FPN model, enhanced with the CT Sequence Selection Algorithm, demonstrated superior performance in classifying ovarian tumors.
  • Comparative evaluation showed the proposed algorithm outperformed existing state-of-the-art methods.
  • The algorithm effectively improved the precision of distinguishing between malignant and early-stage ovarian cancer.

Conclusions:

  • The developed deep learning approach with the CT Sequence Selection Algorithm offers a promising tool for improving early detection of ovarian cancer.
  • This method has the potential to significantly benefit patient outcomes, particularly in resource-limited settings.
  • The research highlights the efficacy of advanced AI in addressing critical challenges in gynaecological cancer diagnosis.