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Development of Compendium for Esophageal Squamous Cell Carcinoma
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Atom Search Optimization with the Deep Transfer Learning-Driven Esophageal Cancer Classification Model.

Nawaf R Alharbe1, Raafat M Munshi2, Manal M Khayyat3

  • 1Applied College, Taibah University, Medina, Saudi Arabia.

Computational Intelligence and Neuroscience
|September 26, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep learning model for accurate esophageal cancer (EC) classification from medical images. The atom search optimization with deep transfer learning-driven EC classification (ASODTL-ECC) model enhances early detection and precision therapy planning.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Esophageal cancer (EC) poses a significant global health challenge.
  • Early detection and precise classification of EC are crucial for effective treatment and improved survival rates.
  • Automated diagnostic tools can enhance physician accuracy, but EC classification is difficult due to similar endoscopic features.

Purpose of the Study:

  • To develop an automated system for timely and accurate esophageal cancer (EC) classification using deep learning.
  • To introduce a novel model, atom search optimization with deep transfer learning-driven EC classification (ASODTL-ECC), for analyzing medical images.
  • To improve diagnostic performance in identifying EC and its stages.

Main Methods:

  • The ASODTL-ECC model utilizes Gaussian filtering (GF) for image preprocessing and quality enhancement.
  • A deep convolution neural network (DCNN) based on the residual network (ResNet) architecture is employed for feature extraction.
  • Atom Search Optimization (ASO) combined with an extreme learning machine (ELM) is used for EC presence identification.

Main Results:

  • The ASODTL-ECC model demonstrated timely and accurate examination of medical images for EC detection.
  • Performance evaluation showed the superiority of the ASODTL-ECC model compared to existing approaches.
  • The study highlights the effectiveness of the integrated DCNN, ASO, and ELM for EC classification.

Conclusions:

  • The developed ASODTL-ECC model offers a promising automated solution for esophageal cancer classification.
  • This approach can aid in improving diagnostic accuracy and facilitating precision therapy planning.
  • The findings suggest potential for enhanced patient outcomes through earlier and more accurate EC detection.