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Research Article

Chest X-ray image classification using transfer learning and hyperparameter customization for lung disease diagnosis

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Received 15 Jun 2023, Accepted 07 Feb 2024, Published online: 05 Mar 2024

Figures & data

Figure 1. Overview of CNN architecture.

Figure 1. Overview of CNN architecture.

Figure 2. CNN based classification.

Figure 2. CNN based classification.

Figure 3. Transformer encoder.

Figure 3. Transformer encoder.

Figure 4. Vision transformer architecture.

Figure 4. Vision transformer architecture.

Figure 5. Fine-tuning approach for new dataset.

Figure 5. Fine-tuning approach for new dataset.

Figure 6. Overview of the proposed method – CovEnViT model.

Figure 6. Overview of the proposed method – CovEnViT model.

Figure 7. The description of implementation the model processing.

Figure 7. The description of implementation the model processing.

Figure 8. Local filter – CovEn block module.

Figure 8. Local filter – CovEn block module.

Figure 9. Transfer learning for X-ray.

Figure 9. Transfer learning for X-ray.

Table 1. Train and validation dataset.

Table 2. Performance metrics of the different models.

Figure 10. Evaluation metrics on the validation set using different models.

Figure 10. Evaluation metrics on the validation set using different models.

Figure 11. Confusion matrices were computed using (a) ViT, (b) DenseNet201, (c) VGG19, (d) Resnet50, (e) CovEnViT.

Figure 11. Confusion matrices were computed using (a) ViT, (b) DenseNet201, (c) VGG19, (d) Resnet50, (e) CovEnViT.

Table 3. Details of classified results of the different models.