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Manta Ray Foraging Optimization Transfer Learning-Based Gastric Cancer Diagnosis and Classification on Endoscopic

Fadwa Alrowais1, Saud S Alotaibi2, Radwa Marzouk3

  • 1Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.

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|November 26, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an automated method for gastric cancer (GC) diagnosis using endoscopic images. The Manta Ray Foraging Optimization Transfer Learning technique achieved 99.25% accuracy in classifying gastric cancer.

Keywords:
deep learningendoscopic imagesgastric cancermedical diagnosistransfer learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Gastric cancer (GC) diagnosis relies heavily on endoscopic imaging.
  • Advancements in computer vision (CV) and deep learning (DL) enable automated diagnostic tools.
  • Accurate and efficient GC detection is crucial for patient outcomes.

Purpose of the Study:

  • To develop an automated technique for gastric cancer diagnosis and classification using endoscopic images.
  • To enhance the accuracy and efficiency of GC detection through novel computational methods.

Main Methods:

  • A Manta Ray Foraging Optimization Transfer Learning technique (MRFOTL-GCDC) was developed.
  • Wiener filtering (WF) was employed for endoscopic image noise removal.
  • Feature extraction utilized Manta Ray Foraging Optimization (MRFO) with the SqueezeNet model.
  • Hyperparameter tuning was optimized using the MRFO algorithm.
  • Elman Neural Network (ENN) was used for final GC classification.

Main Results:

  • The MRFOTL-GCDC technique demonstrated significant improvements in endoscopic image classification.
  • The proposed method achieved a high accuracy rate of 99.25% for gastric cancer classification.
  • Simulation analysis confirmed the enhanced performance compared to existing methods.

Conclusions:

  • The MRFOTL-GCDC technique offers a highly accurate and efficient automated solution for gastric cancer diagnosis from endoscopic images.
  • This approach holds potential for improving early detection and treatment planning in oncology.