A vision transformer machine learning model for COVID-19 diagnosis using chest X-ray images

Chen, Tianyi; Philippi, Ian; Phan, Quoc Bao; Nguyen, Linh; Bui, Ngoc Thang; daCunha, Carlo; Nguyen, Tuy Tan

doi:10.1016/j.health.2024.100332

Cited by 4 publications

References 23 publications

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Novel large empirical study of deep transfer learning for COVID-19 classification based on CT and X-ray images

Almutaani,

Turki,

Taguchi

2024

Sci Rep

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The early and highly accurate prediction of COVID-19 based on medical images can speed up the diagnostic process and thereby mitigate disease spread; therefore, developing AI-based models is an inevitable endeavor. The presented work, to our knowledge, is the first to expand the model space and identify a better performing model among 10,000 constructed deep transfer learning (DTL) models as follows. First, we downloaded and processed 4481 CT and X-ray images pertaining to COVID-19 and non-COVID-19 patients, obtained from the Kaggle repository. Second, we provide processed images as inputs to four pre-trained deep learning models (ConvNeXt, EfficientNetV2, DenseNet121, and ResNet34) on more than a million images from the ImageNet database, in which we froze the convolutional and pooling layers pertaining to the feature extraction part while unfreezing and training the densely connected classifier with the Adam optimizer. Third, we generate and take a majority vote of two, three, and four combinations from the four DTL models, resulting in DTL models. Then, we combine the 11 DTL models, followed by consecutively generating and taking the majority vote of DTL models. Finally, we select DTL models from Experimental results from the whole datasets using five-fold cross-validation demonstrate that the best generated DTL model, named HC, achieving the best AUC of 0.909 when applied to the CT dataset, while ConvNeXt yielded a higher marginal AUC of 0.933 compared to 0.93 for HX when considering the X-ray dataset. These promising results set the foundation for promoting the large generation of models (LGM) in AI. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-024-76498-4.

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Novel large empirical study of deep transfer learning for COVID-19 classification based on CT and X-ray images

Almutaani,

Turki,

Taguchi

2024

Sci Rep

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Advances in medical image analysis: A comprehensive survey of lung infection detection

Kordnoori,

Sabeti,

Mostafaei

et al. 2024

IET Image Processing

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This research investigates advanced approaches in medical image analysis, specifically focusing on segmentation and classification techniques, as well as their integration into multi‐task architectures for lung infections. This research begins by explaining key architectural models used in segmentation and classification tasks. The study extends to the enhancement of these architectures through attention modules and conditional random fields. Relevant datasets and evaluation metrics, incorporating discussions on loss functions are also reviewed. This review encompasses recent advancements in single‐task and multi‐task models, highlighting innovations in semi‐supervised, self‐supervised, few‐shot, and zero‐shot learning techniques. Empirical analysis is conducted on both single‐task and multi‐task architectures, predominantly utilizing the U‐Net framework, and is applied across multiple datasets for segmentation and classification tasks. Results demonstrate the effectiveness of these models and provide insights into the strengths and limitations of different approaches. This research contributes to improved detection and diagnosis of lung infections by offering a comprehensive overview of current methodologies and their practical applications.

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Novel Large Empirical Study of Deep Transfer Learning for COVID-19 Classification Based on CT and X-Ray Images

Almutaani,

Turki,

Taguchi

2024

Preprint

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Early and accurate prediction of COVID-19 based on medical images can speed up the diagnostic process and thereby mitigating the disease spread. Hence, developing AI-based models is an inevitable endeavor. The presented work, to our knowledge, is considered the largest empirical COVID-19 classification study using CT and X-ray in which we propose a novel computational framework constructing 10000 deep transfer learning (DTL) models as follows. First, we downloaded and processed 4481 CT and X-ray images pertaining to COVID-19 and non-COVID-19 patients, obtained from the Kaggle repository. Second, we provide processed images as input to four pre-trained deep learning models on more than million images from ImageNet database in which we froze the convolutional and pooling layers pertaining to the feature extraction part while unfroze and train the densely connected classifier with Adam optimizer. Third, we generate and take majority vote of 2, 3, and 4-combinations from the 4 DTL models, resulted inmodels. Then, we combine the 11 DTL models, followed by consecutively generating and taking the majority vote ofDTL models. Finally, we select 7953 DTL models from. Experimental results on the whole datasets using five-fold cross-validation demonstrate that best generated DTL model, named HC, achieving the best AUC of 0.909 when applied to the CT dataset while ConvNeXt yielded a higher marginal AUC of 0.933 compared to 0.93 for HX when considering the X-ray dataset. These promising results set the foundation for promoting large generation of models (LGM) in AI.

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A vision transformer machine learning model for COVID-19 diagnosis using chest X-ray images

Cited by 4 publications

References 23 publications

Novel large empirical study of deep transfer learning for COVID-19 classification based on CT and X-ray images

Novel large empirical study of deep transfer learning for COVID-19 classification based on CT and X-ray images

Advances in medical image analysis: A comprehensive survey of lung infection detection

Novel Large Empirical Study of Deep Transfer Learning for COVID-19 Classification Based on CT and X-Ray Images

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