Stacking Ensemble and ECA-EfficientNetV2 Convolutional Neural Networks on Classification of Multiple Chest Diseases Including COVID-19

Huang, Meiling; Liao, Yu-Chieh

doi:10.1016/j.acra.2022.11.027

Cited by 20 publications

(6 citation statements)

References 42 publications

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“…“Stacking Ensemble and ECA-EfficientNetV2 Convolutional Neural Networks on Classification of Multiple Chest Diseases Including COVID-19” by Huang and Liao ( 2022 ), Stacking-ensemble model, which combines six pre-trained models: EfficientNetV2-B0, EfficientNetV2-B1, EfficientNetV2-B2, EfficientNetV2-B3, EfficientNetV2-S, and EfficientNetV2-M. Based on ECA-Net and EfficientNetV2, the second model is a self-designed model called ECA-EfficientNetV2. On chest X-ray and CT images, each model underwent ten-fold cross-validation.…”

Section: Resultsmentioning

confidence: 99%

Stacked ensemble deep learning for pancreas cancer classification using extreme gradient boosting

Bakasa,

Viriri

2023

Front. Artif. Intell.

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Ensemble learning aims to improve prediction performance by combining several models or forecasts. However, how much and which ensemble learning techniques are useful in deep learning-based pipelines for pancreas computed tomography (CT) image classification is a challenge. Ensemble approaches are the most advanced solution to many machine learning problems. These techniques entail training multiple models and combining their predictions to improve the predictive performance of a single model. This article introduces the idea of Stacked Ensemble Deep Learning (SEDL), a pipeline for classifying pancreas CT medical images. The weak learners are Inception V3, VGG16, and ResNet34, and we employed a stacking ensemble. By combining the first-level predictions, an input train set for XGBoost, the ensemble model at the second level of prediction, is created. Extreme Gradient Boosting (XGBoost), employed as a strong learner, will make the final classification. Our findings showed that SEDL performed better, with a 98.8% ensemble accuracy, after some adjustments to the hyperparameters. The Cancer Imaging Archive (TCIA) public access dataset consists of 80 pancreas CT scans with a resolution of 512 * 512 pixels, from 53 male and 27 female subjects. A sample of two hundred and twenty-two images was used for training and testing data. We concluded that implementing the SEDL technique is an effective way to strengthen the robustness and increase the performance of the pipeline for classifying pancreas CT medical images. Interestingly, grouping like-minded or talented learners does not make a difference.

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Section: Resultsmentioning

confidence: 99%

Stacked ensemble deep learning for pancreas cancer classification using extreme gradient boosting

Bakasa,

Viriri

2023

Front. Artif. Intell.

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“…It is presented in Table 19. We have also compared our best TL with other existing models proposed by Alshazly et al [28], Cruz et al [45], Shaik et al [30], and Huang et al [31], who achieved accuracies of 92.9%, 82.76%, 97.38%, and 95.66%, respectively. Our results demonstrate the effectiveness of TL in developing accurate and efficient models for COVID-19 diagnosis using CT images.…”

Section: Benchmarkingmentioning

confidence: 94%

“…Other ensemble models are also shown in Table 20 and are quite lower than our proposed model. Other EDL models were proposed by Pathan et al [33], Kundu et al [34], Cruz et al [29], Shaik et al [30], Khanibadi et al [138], Lu et al [139], and Huang et al [31]. We also performed scientific validation that is missing in other models.…”

Section: Benchmarkingmentioning

confidence: 94%

“…The best accuracy metrics were 82.76%, precision was 85.39%, and AUC was 0.89 using DenseNet161; the second-best model is VGG16, for which accuracy was 81.77%, precision was 79.05%, and AUC was 0.9. Shaik et al [30], Huang et al [31], and Xu et al [32] also used TL-based MobileNetV2 on Dataset COVID-CT, TL-based MobileNetV2 on SARS-CoV2, and TL-based EfficientNetV2m on COVID-CT, and they achieved accuracies of 97.38%, 88.67%, and 95.66%, respectively. EDL has a major role in improving detection accuracy.…”

Section: Background Literaturementioning

confidence: 99%

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Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework

Chabert

Carriero

et al. 2023

Diagnostics

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Background and motivation: Lung computed tomography (CT) techniques are high-resolution and are well adopted in the intensive care unit (ICU) for COVID-19 disease control classification. Most artificial intelligence (AI) systems do not undergo generalization and are typically overfitted. Such trained AI systems are not practical for clinical settings and therefore do not give accurate results when executed on unseen data sets. We hypothesize that ensemble deep learning (EDL) is superior to deep transfer learning (TL) in both non-augmented and augmented frameworks. Methodology: The system consists of a cascade of quality control, ResNet–UNet-based hybrid deep learning for lung segmentation, and seven models using TL-based classification followed by five types of EDL’s. To prove our hypothesis, five different kinds of data combinations (DC) were designed using a combination of two multicenter cohorts—Croatia (80 COVID) and Italy (72 COVID and 30 controls)—leading to 12,000 CT slices. As part of generalization, the system was tested on unseen data and statistically tested for reliability/stability. Results: Using the K5 (80:20) cross-validation protocol on the balanced and augmented dataset, the five DC datasets improved TL mean accuracy by 3.32%, 6.56%, 12.96%, 47.1%, and 2.78%, respectively. The five EDL systems showed improvements in accuracy of 2.12%, 5.78%, 6.72%, 32.05%, and 2.40%, thus validating our hypothesis. All statistical tests proved positive for reliability and stability. Conclusion: EDL showed superior performance to TL systems for both (a) unbalanced and unaugmented and (b) balanced and augmented datasets for both (i) seen and (ii) unseen paradigms, validating both our hypotheses.

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“…E cientnetV2, as a newly proposed classi cation network, increases the network width, depth, and resolution to improve the performance [27,28]. It has been used in plant disease detection [29,30], mechanical fault diagnosis [31], and some other elds [32,33].…”

Section: Introductionmentioning

confidence: 99%

Damage identification of wire rope under noise background via Light- EfficientNetV2 and Magnetic flux leakage image

Huang

Wang

Yang

et al. 2023

Preprint

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Magnetic flux leakage (MFL) testing, non-destructive testing, can prevent some major accidents of hoist equipment by identifying the damage of wire ropes. However, in harsh working conditions such as mines and oil wells, the inevitable vibration and swing of wire rope will generate noise and interfere with the MFL signal, which makes us difficult to identify the damage. As a classification network, Convolutional neural network (CNN) is positive in recognition accuracy and noise resistance, but it hardly uses in wire rope damage classification. To improve the accuracy of wire rope damage identification under noise background, we propose a method of wire rope damage identification via Light-EfficientNetV2 and MFL image. First, the MFL signal is segmented and rearranged to form the MFL image, and then the image is classified by Light-EfficientNetV2. To improve the classification efficiency, we reduce the layers of EfficientNetV2 to make it lighter. Finally, the availability of this method is proved by the validation set. Compared with four neural networks, the accuracy is the highest. Moreover, as the noise increased, the accuracy of Light-EfficientNetV2 is higher than EfficientNetV2, which has application value in the wire rope damage identification under noise background.

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Stacking Ensemble and ECA-EfficientNetV2 Convolutional Neural Networks on Classification of Multiple Chest Diseases Including COVID-19

Cited by 20 publications

References 42 publications

Stacked ensemble deep learning for pancreas cancer classification using extreme gradient boosting

Stacked ensemble deep learning for pancreas cancer classification using extreme gradient boosting

Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework

Damage identification of wire rope under noise background via Light- EfficientNetV2 and Magnetic flux leakage image

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