Diagnosis of Pediatric Pneumonia with Ensemble of Deep Convolutional Neural Networks in Chest X-Ray Images

Ayan, Enes; Karabulut, Bergen; Ünver, Halil Murat

doi:10.1007/s13369-021-06127-z

Cited by 52 publications

(32 citation statements)

References 37 publications

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“…Initially, we chose the pretrained models from the previous research, as in [26,52]. The best three methods were selected after comparing their accuracy to other methods using the previously mentioned free dataset of chest X-ray images.…”

Section: Results and Analysismentioning

confidence: 99%

“…There are two kinds of ensemble techniques utilized in CNN architectures [26]. In the first technique, some researchers employed different CNN algorithms to obtain features from the medical images, as in [48].…”

Section: Deep Convolutional Neural Network (Dcnn) Modelsmentioning

confidence: 99%

“…The performance of the proposed classification method was evaluated based on precision, recall, F1-score, and accuracy, as introduced in Equations ( 2)-( 5), respectively. These metrics are the most popular in medical image classification [26,49]. The precision is measured as the percentage of exact data that conform to specified characteristics.…”

Section: Data Set Descriptionmentioning

confidence: 99%

“…Furthermore, VIT achieves remarkable results compared to CNN while it obtains fewer computational resources for pretraining [23]. Since the existing approaches merge CNN models for creating an ensemble method without using the transformers, as in [26], we have decided to combine both to obtain a methodology that simultaneously merges and improves each individual proposal-that is, we propose a method that joins VIT and the selection of the best CNN models for the training stage. In addition, the obtained features from the selected three models were combined using a probability-based ensemble approach to achieve good classification performance.…”

Section: Introductionmentioning

confidence: 99%

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Pneumonia Detection on Chest X-ray Images Using Ensemble of Deep Convolutional Neural Networks

et al. 2022

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Pneumonia is a life-threatening lung infection resulting from several different viral infections. Identifying and treating pneumonia on chest X-ray images can be difficult due to its similarity to other pulmonary diseases. Thus, the existing methods for predicting pneumonia cannot attain substantial levels of accuracy. This paper presents a computer-aided classification of pneumonia, coined Ensemble Learning (EL), to simplify the diagnosis process on chest X-ray images. Our proposal is based on Convolutional Neural Network (CNN) models , which are pretrained CNN models that have been recently employed to enhance the performance of many medical tasks instead of training CNN models from scratch. We propose to use three well-known CNNs (DenseNet169, MobileNetV2, and Vision Transformer) pretrained using the ImageNet database.

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Section: Results and Analysismentioning

confidence: 99%

Section: Deep Convolutional Neural Network (Dcnn) Modelsmentioning

confidence: 99%

Section: Data Set Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pneumonia Detection on Chest X-ray Images Using Ensemble of Deep Convolutional Neural Networks

et al. 2022

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“…It is primarily caused by bacterial and viral infections and its primary clinical manifestations include dyspnea, fever, cough, and shortness of breath. 2,3 at 37°C. Cells were stimulated with increasing dose of GPF.…”

Section: Introductionmentioning

confidence: 99%

6'-O-galloylpaeoniflorin alleviates inflammation and oxidative stress in pediatric pneumonia through activating Nrf2 activation

Song²,

Zhang³

et al. 2022

Allergol Immunopathol

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Objective: To assess the therapeutic effect and mechanism of 6'-o-galloylpaeoniflorin (GPF) in pediatric pneumonia. Methods: The effects of lipopolysaccharide (LPS) and GPF on cell viability and apoptosis were examined by cell counting kit-8 assay and flow cytometry analysis. The oxidative stress and inflammatory response were assessed by detecting expression levels of superoxide dismutase, glutathione, r-glutamyl cysteingl+glycine, myeloperoxidase, and malondialdehyde as well as tumor necrosis factor-α, Interleukin-18, and Interleukin-10 by using enzyme-linked-immunosorbent serologic assay. Moreover, the activation of nuclear factor erythroid 2-related factor 2 (Nrf2) pathway was detected by immunoblot assay, and the influence of Nrf2-knockdown on cell viability, oxidative stress, and inflammation response was also investigated. Results: The results established that GPF increased the viability of LPS-induced pneumonia cells. In addition, GPF reduced LPS-induced oxidative stress in pneumonia cells. It was further discovered that GPF reduced LPS-induced inflammation in pneumonic cell. GPF improved the activity of Nrf2 in LPS-treated pneumonic cells, and therefore alleviated inflammation and oxidative stress in pediatric pneumonia. Conclusion: GPF could serve as a promising drug for treating pediatric pneumonia.

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Dental student application of artificial intelligence technology in detecting proximal caries lesions

Ayan,

Bayraktar,

Çelik

et al. 2024

Journal of Dental Education

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ObjectivesThis study aimed to investigate the caries diagnosis performances of dental students after training with an artificial intelligence (AI) application utilizing deep learning techniques, a type of artificial neural network.MethodsA total of 1200 bitewing radiographs were obtained from the institution's database and two specialist dentists labeled the caries lesions in the images. Randomly selected 1000 images were used for training purposes and the remaining 200 radiographs were used to evaluate the caries diagnostic performance of the AI. Then, a convolutional neural network, a deep learning algorithm commonly employed to analyze visual imagery problems, called “You Only Look Once,” was modified and trained to detect enamel and dentin caries lesions in the radiographs. Forty dental students were selected voluntarily and randomly divided into two groups. The pre‐test results of dental caries diagnosis performances of both groups were recorded. After 1 week, group 2 students were trained using an AI application. Then, the post‐test results of both groups were recorded. The labeling duration of the students was also measured and analyzed.ResultsWhen both groups’ pre‐test and post‐test results were evaluated, a statistically significant improvement was found for all parameters examined except precision score (p < 0.05). However, the trained group's accuracy, sensitivity, specificity, and F1 scores were significantly higher than the non‐trained group in terms of post‐test scores (p < 0.05). In group 2 (trained group), the post‐test labeling time was considerably increased (p < 0.05).ConclusionsThe students trained by AI showed promising results in detecting caries lesions. The use of AI can also contribute to the clinical education of dental students.

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Diagnosis of Pediatric Pneumonia with Ensemble of Deep Convolutional Neural Networks in Chest X-Ray Images

Cited by 52 publications

References 37 publications

Pneumonia Detection on Chest X-ray Images Using Ensemble of Deep Convolutional Neural Networks

Pneumonia Detection on Chest X-ray Images Using Ensemble of Deep Convolutional Neural Networks

6'-O-galloylpaeoniflorin alleviates inflammation and oxidative stress in pediatric pneumonia through activating Nrf2 activation

Dental student application of artificial intelligence technology in detecting proximal caries lesions

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