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A Circular Box-Based Deep Learning Model for the Identification of Signet Ring Cells from Histopathological Images.

Saleh Albahli1, Tahira Nazir2

  • 1Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia.

Bioengineering (Basel, Switzerland)
|October 28, 2023
PubMed
Summary
This summary is machine-generated.

Early detection of signet ring cell (SRC) carcinoma is challenging but crucial for patient survival. This study introduces a deep learning model, CircleNet with ResNet-34, achieving 96.40% accuracy in recognizing SRCs.

Keywords:
CircleNetDenseNethistopathological imagesmedical imagingsignet ring cell

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Signet ring cell (SRC) carcinoma is an aggressive cancer often diagnosed late.
  • Early detection is difficult due to variable cell morphology and imaging conditions.
  • Timely diagnosis significantly impacts treatment outcomes and patient survival rates.

Purpose of the Study:

  • To develop an automated, accurate, and cost-effective method for early recognition and classification of SRC carcinoma.
  • To address the challenges posed by deceptive onset and late-stage diagnosis of SRC carcinoma.

Main Methods:

  • A deep learning (DL) approach utilizing a custom CircleNet model integrated with ResNet-34 architecture.
  • Application of data augmentation techniques to enhance a challenging dataset of 35,000 images.
  • Comparative analysis against existing methods to validate performance.

Main Results:

  • The proposed CircleNet with ResNet-34 model achieved a high accuracy of 96.40% in SRC recognition and classification.
  • The DL-based method demonstrated superior performance compared to other evaluated methods.
  • The model effectively handles variations in cell size, shape, and illumination.

Conclusions:

  • The developed deep learning methodology offers a promising solution for the early and accurate detection of signet ring cell carcinoma.
  • This automated approach can potentially reduce diagnostic costs and improve patient prognosis.
  • Further research can explore integration into clinical workflows for real-time diagnostic support.