Classification of COVID-19 from community-acquired pneumonia: Boosting the performance with capsule network and maximum intensity projection image of CT scans

Wu, Yanan; Qi, Qianqian; Qi, Shouliang; Yang, Liming; Wang, Hanlin; Yu, Hui; Li, Jianpeng; Wang, Gang; Zhang, Ping; Liang, Zhenyu; Chen, Rongchang

doi:10.1016/j.compbiomed.2023.106567

Cited by 5 publications

(2 citation statements)

References 63 publications

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“… 45 Existing studies mostly focus on the diagnosis of specific pathogens, which is crucial for decision-making regarding specific types of pneumonia in pandemics. 46 , 47 , 48 , 49 A deep learning-based automated detection algorithm developed using 60,989 CXR scans was used to identify active PTB and performed better than human experts. 50 Another AI model based on a multicountry chest CT dataset automatically located the parietal pleura and lung parenchyma and classified COVID-19 pneumonia patients with an accuracy of 90.8%.…”

Section: Discussionmentioning

confidence: 99%

A multimodal integration pipeline for accurate diagnosis, pathogen identification, and prognosis prediction of pulmonary infections

Shao,

Ma,

et al. 2024

The Innovation

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Section: Discussionmentioning

confidence: 99%

A multimodal integration pipeline for accurate diagnosis, pathogen identification, and prognosis prediction of pulmonary infections

Shao,

Ma,

et al. 2024

The Innovation

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“…These studies were pivotal in identifying the unique radiographic features of COVID-19, such as groundglass opacities and bilateral infiltrates, and how AI models could be trained to recognize these features with high accuracy. The challenge of dataset diversity and size was addressed in studies [20], [21], and [22], emphasizing the importance of comprehensive datasets in developing robust CNN models. These works discussed how a diverse range of X-ray images, including data augmentation techniques, could enhance the model's ability to generalize across different presentations of COVID-19.…”

Section: Related Workmentioning

confidence: 99%

2D-CNN Architecture for Accurate Classification of COVID-19 Related Pneumonia on X-Ray Images

Dzhaynakbaev,

Kurmanbekkyzy,

Baimakhanova

et al. 2024

IJACSA

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In the wake of the COVID-19 pandemic, the use of medical imaging, particularly X-ray radiography, has become integral to the rapid and accurate diagnosis of pneumonia induced by the virus. This research paper introduces a novel twodimensional Convolutional Neural Network (2D-CNN) architecture specifically tailored for the classification of COVID-19 related pneumonia in X-ray images. Leveraging the advancements in deep learning, our model is designed to distinguish between viral pneumonia, typical of COVID-19, and other types of pneumonia, as well as healthy lung imagery. The architecture of the proposed 2D-CNN is characterized by its depth and a unique layer arrangement, which optimizes feature extraction from X-ray images, thus enhancing the model's diagnostic precision. We trained our model using a substantial dataset comprising thousands of annotated X-ray images, including those of patients diagnosed with COVID-19, patients with other pneumonia types, and individuals with no lung infection. This dataset enabled the model to learn a wide range of radiographic features associated with different lung conditions. Our model demonstrated exceptional performance, achieving high accuracy, sensitivity, and specificity in preliminary tests.The results indicate that our 2D-CNN model not only outperforms existing pneumonia classification models but also provides a valuable tool for healthcare professionals in the early detection and differentiation of COVID-19 related pneumonia. This capability is crucial for prompt and appropriate treatment, potentially reducing the pandemic's burden on healthcare systems. Furthermore, the model's design allows for easy integration into existing medical imaging workflows, offering a practical and efficient solution for frontline medical facilities. Our research contributes to the ongoing efforts to combat COVID-19 by enhancing diagnostic procedures through the application of artificial intelligence in medical imaging.

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Automatic detection and proximity quantification of inferior alveolar nerve and mandibular third molar on cone-beam computed tomography

Huang,

Wang,

Wang

et al. 2024

Clin Oral Invest

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Classification of COVID-19 from community-acquired pneumonia: Boosting the performance with capsule network and maximum intensity projection image of CT scans

Cited by 5 publications

References 63 publications

A multimodal integration pipeline for accurate diagnosis, pathogen identification, and prognosis prediction of pulmonary infections

A multimodal integration pipeline for accurate diagnosis, pathogen identification, and prognosis prediction of pulmonary infections

2D-CNN Architecture for Accurate Classification of COVID-19 Related Pneumonia on X-Ray Images

Automatic detection and proximity quantification of inferior alveolar nerve and mandibular third molar on cone-beam computed tomography

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