The Detection of COVID-19 in Chest X-Rays Using Ensemble CNN Techniques

Kuzinkovas, Domantas; Clement, Sandhya

doi:10.1101/2022.11.29.22282856

Cited by 2 publications

(2 citation statements)

References 38 publications

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“…The main aim of ensemble learning is to improve the overall performance of classifiers by combining the predictions of individual neural network models. Ensemble learning has recently gained popularity in image classification using deep learning [12][13][14]. We trained VGG16, VGG19 and DenseNet201 on the Mendeley medicinal leaf dataset and evaluated the efficiency of these component models.…”

Section: Introductionmentioning

confidence: 99%

An Effective Ensemble Convolutional Learning Model with Fine-tuning for Medicinal Plant Leaf Identification

Hajam,

Arif,

Khanday

et al. 2023

Preprint

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Accurate and efficient medicinal plant image classification is of utmost importance as these plants produce a wide variety of bioactive compounds that offer therapeutic benefits. With a long history of medicinal plant usage, different parts of plants, such as flowers, leaves, and roots, have been recognized for their medicinal properties and are used for plant identification. However, leaf images are extensively used due to their convenient accessibility and are a major source of information. In recent years, transfer learning and fine-tuning, which use pre-trained deep convolutional networks to extract pertinent features, has emerged as an extremely effective approach for image identification problems. This study is leveraging the power of three component deep convolutional neural networks, namely VGG16, VGG19 and DenseNet201, to derive features from the input images of the medicinal plant dataset, containing leaf images of 30 classes. The models were compared and ensembled to make four hybrid models to enhance the predictive performance by utilizing the averaging and weighted averaging strategies. Quantitative experiments were carried out to evaluate the models on the Mendeley medicinal leaf dataset. The resultant ensemble of VGG19+DensNet201 with fine-tuning showcases enhanced capability in identifying medicinal plant images with an improvement of 7.43% and 5.8% compared with VGG19 and VGG16. Furthermore, VGG19+DensNet201 can outperform its standalone counterparts by achieving an accuracy of 99.12% on the test set. A thorough assessment with metrics such as accuracy, recall, precision, and f1-score firmly establishes the effectiveness of the ensemble strategy.

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Section: Introductionmentioning

confidence: 99%

An Effective Ensemble Convolutional Learning Model with Fine-tuning for Medicinal Plant Leaf Identification

Hajam,

Arif,

Khanday

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…This technique provides an accuracy of 96.48% for image classification. In study [23] using ensemble method, Kuzinkovas et al suggested a model where ANN, LR, LDA and RF performs the task of image classification with an accuracy of 98.34%. During the classification task, the ensemble model uses ResNet50, VGG19, VGG16, and GLCM for feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Advanced Detection of COVID-19 Through X-ray Imaging using CovidFusionNet with Hybrid CNN Fusion and Multi-resolution Analysis

Khalid

2023

IJACSA

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The rapid diagnosis of COVID-19 through imaging is crucial in the current pandemic scenario. This study introduces the CovidFusionNet, a novel model adapted for efficient COVID-19 image classification. By effectively combining fusing features from seven pre-trained convolutional neural networks (CNNs), our model presents better accuracy in detecting COVID-19 from X-ray images. Three separate datasets, obtained from Kaggle, were used in this study to ensure the reliability and robustness of the model. The Continuous and Discrete Wavelet Transform was implemented for robust multi-resolution image analysis to maintain image properties after denoising. A novel enhancement method was also proposed, combining the capabilities of Adaptive Histogram Equalization (AHE) and Wavelet Transforms to emphasize finer details and concurrently heighten clarity while minimizing noise. Furthermore, to mitigate class imbalance, an oversampling approach was implemented. Comprehensive validation using 12 metrics across each dataset verified the proposed consistent performance, with remarkable accuracies of 98.02% for Dataset One, 99.30% for Dataset Two, and 98.25% for Dataset Three. Comparing CovidFusionNet against seven well-known pre-trained models showed that CovidFusionNet appeared more capable. This research advances the area of image-based diagnosis using COVID-19 and provides a model for quick medical actions.

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The Detection of COVID-19 in Chest X-Rays Using Ensemble CNN Techniques

Cited by 2 publications

References 38 publications

An Effective Ensemble Convolutional Learning Model with Fine-tuning for Medicinal Plant Leaf Identification

An Effective Ensemble Convolutional Learning Model with Fine-tuning for Medicinal Plant Leaf Identification

Advanced Detection of COVID-19 Through X-ray Imaging using CovidFusionNet with Hybrid CNN Fusion and Multi-resolution Analysis

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