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CT scan pancreatic cancer segmentation and classification using deep learning and the tunicate swarm algorithm.

Hari Prasad Gandikota1, Abirami S1, Sunil Kumar M2

  • 1Department of Computer Science & Engineering, Annamalai University, Chidambaram, Tamilnadu, India.

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Summary

This study introduces a novel deep learning technique for pancreatic cancer (PC) classification in CT scans. The TSADL-PCSC method enhances diagnostic accuracy by combining advanced segmentation and classification models.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Pancreatic cancer (PC) is a highly lethal disease with poor survival rates, necessitating accurate and timely diagnosis.
  • Computed tomography (CT) scans are crucial for visualizing pancreatic tissue, aiding in the discrimination between cancerous and non-cancerous regions.
  • Machine learning (ML) and deep learning (DL) show promise in automating and improving PC classification from CT scans.

Purpose of the Study:

  • To develop an accurate and efficient deep learning model for pancreatic cancer segmentation and classification in CT scans.
  • To enhance the diagnostic performance of pancreatic cancer detection through an automated approach.

Main Methods:

  • The proposed Tunicate Swarm Algorithm with Deep Learning-based Pancreatic Cancer Segmentation and Classification (TSADL-PCSC) technique was developed.
  • W-Net was utilized for precise segmentation of affected pancreatic regions in CT scans.
  • GhostNet served as a feature extractor, and a Deep Echo State Network (DESN) was employed for classification, with hyperparameters tuned by the Tunicate Swarm Algorithm (TSA).

Main Results:

  • The TSADL-PCSC technique demonstrated significant improvements in pancreatic cancer classification accuracy compared to existing methods.
  • Experimental results on a benchmark CT scan database validated the effectiveness of the proposed approach.

Conclusions:

  • The TSADL-PCSC technique offers a promising, accurate, and automated solution for pancreatic cancer classification using CT scans.
  • This advanced deep learning approach has the potential to significantly improve diagnostic outcomes for pancreatic cancer patients.