research-article

Atom Search Optimization with the Deep Transfer Learning-Driven Esophageal Cancer Classification Model

Authors:

Nawaf R. Alharbe,

Raafat M. Munshi,

Manal M. Khayyat,

Mashael M. Khayyat,

Saadia Hassan Abdalaha Hamza,

Abeer A. Aljohani Academic Editor:

Laxmi LydiaAuthors Info & Claims

Computational Intelligence and Neuroscience, Volume 2022

https://doi.org/10.1155/2022/4629178

Published: 01 January 2022 Publication History

Abstract

Esophageal cancer (EC) is a commonly occurring malignant tumor that significantly affects human health. Earlier recognition and classification of EC or premalignant lesions can result in highly effective targeted intervention. Accurate detection and classification of distinct stages of EC provide effective precision therapy planning and improve the 5-year survival rate. Automated recognition of EC can aid physicians in improving diagnostic performance and accuracy. However, the classification of EC is challenging due to identical endoscopic features, like mucosal erosion, hyperemia, and roughness. The recent developments of deep learning (DL) and computer-aided diagnosis (CAD) models have been useful for designing accurate EC classification models. In this aspect, this study develops an atom search optimization with a deep transfer learning-driven EC classification (ASODTL-ECC) model. The presented ASODTL-ECC model mainly examines the medical images for the existence of EC in a timely and accurate manner. To do so, the presented ASODTL-ECC model employs Gaussian filtering (GF) as a preprocessing stage to enhance image quality. In addition, the deep convolution neural network- (DCNN-) based residual network (ResNet) model is applied as a feature extraction approach. Besides, ASO with an extreme learning machine (ELM) model is utilized for identifying the presence of EC, showing the novelty of the work. The performance of the ASODTL-ECC model is assessed and compared with existing models under several medical images. The experimental results pointed out the improved performance of the ASODTL-ECC model over recent approaches.

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Cited By

Rai HYoo JRazaque A(2024)Comparative analysis of machine learning and deep learning models for improved cancer detectionExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124838255:PDOnline publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.124838
Intelligence and Neuroscience C(2023)RetractedComputational Intelligence and Neuroscience10.1155/2023/98707892023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/9870789

Index Terms

Atom Search Optimization with the Deep Transfer Learning-Driven Esophageal Cancer Classification Model
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

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Information

Published In

cover image Computational Intelligence and Neuroscience

Computational Intelligence and Neuroscience Volume 2022, Issue

2022

32389 pages

ISSN:1687-5265

EISSN:1687-5273

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Copyright © 2022 Nawaf R. Alharbe et al.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Publisher

Hindawi Limited

London, United Kingdom

Publication History

Published: 01 January 2022

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Cited By

Rai HYoo JRazaque A(2024)Comparative analysis of machine learning and deep learning models for improved cancer detectionExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124838255:PDOnline publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.124838
Intelligence and Neuroscience C(2023)RetractedComputational Intelligence and Neuroscience10.1155/2023/98707892023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/9870789

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