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. 2022 Sep 16:2022:4629178.
doi: 10.1155/2022/4629178. eCollection 2022.

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

Nawaf R Alharbe  1 , Raafat M Munshi  2 , Manal M Khayyat  3 , Mashael M Khayyat  4 , Saadia Hassan Abdalaha Hamza  5 , Abeer A Aljohani  1
Affiliations

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

Nawaf R Alharbe et al. Comput Intell Neurosci. .

Retraction in

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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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Block diagram of the ASODTL-ECC technique.
Figure 2
Figure 2
Structure of residual learning.
Figure 3
Figure 3
Framework of the ELM method.
Figure 4
Figure 4
Sample images.
Figure 5
Figure 5
Confusion matrices of the ASODTL-ECC technique: (a) entire dataset, (b) 70% of TD data, and (c) 30% of TS data.
Figure 6
Figure 6
Result analysis of the ASODTL-ECC technique under entire dataset.
Figure 7
Figure 7
Result analysis of the ASODTL-ECC technique under 70% of TR data.
Figure 8
Figure 8
Result analysis of the ASODTL-ECC technique under 30% of TS dataset.
Figure 9
Figure 9
TA and VA analysis of the ASODTL-ECC technique.
Figure 10
Figure 10
TL and VL analysis of the ASODTL-ECC technique.
Figure 11
Figure 11
Precision-recall curve analysis of the ASODTL-ECC technique.
Figure 12
Figure 12
ROC curve analysis of the ASODTL-ECC technique.
Figure 13
Figure 13
Comparative analysis of the ASODTL-ECC technique with recent algorithms.
Figure 14
Figure 14
Accu y analysis of the ASODTL-ECC technique with recent algorithms.
Algorithm 1
Algorithm 1
Pseudocode of ASO algorithm.
See this image and copyright information in PMC

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