The forgotten view: Chest X-ray - Lateral view

Ittyachen, Abraham M; Vijayan, Anuroopa; Isac, Megha

doi:10.1016/j.rmcr.2017.09.009

Cited by 5 publications

(4 citation statements)

References 9 publications

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“…There is another CXR imaging view called L, standing for Lateral, which is an adjunct for the main frontal view image. Lateral CXR is performed when there is diagnosis uncertainty using frontal CXR [ 21 ]. Thus it is not as common as frontal CXR, and due to its different angle, it is excluded from our data.…”

Section: Dataset Overviewmentioning

confidence: 99%

COVID-CXNet: Detecting COVID-19 in frontal chest X-ray images using deep learning

Haghanifar

Majdabadi

Choi

et al. 2022

Multimed Tools Appl

110

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One of the primary clinical observations for screening the novel coronavirus is capturing a chest x-ray image. In most patients, a chest x-ray contains abnormalities, such as consolidation, resulting from COVID-19 viral pneumonia. In this study, research is conducted on efficiently detecting imaging features of this type of pneumonia using deep convolutional neural networks in a large dataset. It is demonstrated that simple models, alongside the majority of pretrained networks in the literature, focus on irrelevant features for decision-making. In this paper, numerous chest x-ray images from several sources are collected, and one of the largest publicly accessible datasets is prepared. Finally, using the transfer learning paradigm, the well-known CheXNet model is utilized to develop COVID-CXNet. This powerful model is capable of detecting the novel coronavirus pneumonia based on relevant and meaningful features with precise localization. COVID-CXNet is a step towards a fully automated and robust COVID-19 detection system.

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Section: Dataset Overviewmentioning

confidence: 99%

COVID-CXNet: Detecting COVID-19 in frontal chest X-ray images using deep learning

Haghanifar

Majdabadi

Choi

et al. 2022

Multimed Tools Appl

110

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show abstract

“…There is another CXR imaging view called L view, standing for Lateral, which is an adjunct for the main frontal view image. Lateral CXR is performed when there is diagnosis uncertainty using frontal CXR [39], thus it is not as common as frontal CXR and due to its different angel, it is excluded from our data. The third CXR view, called AP supine or AP erect, is an alternative for PA view, usually taken from the patients who are generally too unwell that can not leave the bed to sit or stand.…”

Section: Data and Prepossessingmentioning

confidence: 99%

COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep Learning

Haghanifar¹,

Majdabadi²,

Choi³

et al. 2020

Preprint

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One of the primary clinical observations for screening the infectious by the novel coronavirus is capturing a chest x-ray image. In most of the patients, a chest x-ray contains abnormalities, such as consolidation, which are the results of COVID-19 viral pneumonia. In this study, research is conducted on efficiently detecting imaging features of this type of pneumonia using deep convolutional neural networks in a large dataset. It is demonstrated that simple models, alongside the majority of pretrained networks in the literature, focus on irrelevant features for decision-making. In this paper, numerous chest x-ray images from various sources are collected, and the largest publicly accessible dataset is prepared. Finally, using the transfer learning paradigm, the well-known CheXNet model is utilized for developing COVID-CXNet. This powerful model is capable of detecting the novel coronavirus pneumonia based on relevant and meaningful features with precise localization. COVID-CXNet is a step towards a fully automated and robust COVID-19 detection system.

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“…There are specific cases in which the lateral view provides information for diagnosis that isn't clear or visible on the PA view (Shiraishi et al, 2007;Feigin, 2010;Ittyachen et al, 2017). For example, up to 15% of the lung can be obscured by cardiovascular structures and the diaphragm (Raoof et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Quantifying the Value of Lateral Views in Deep Learning for Chest X-rays

Hashir,

Bertrand,

Cohen

2020

Preprint

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Most deep learning models in chest X-ray prediction utilize the posteroanterior (PA) view due to the lack of other views available. PadChest is a large-scale chest X-ray dataset that has almost 200 labels and multiple views available. In this work, we use PadChest to explore multiple approaches to merging the PA and lateral views for predicting the radiological labels associated with the X-ray image. We find that different methods of merging the model utilize the lateral view differently. We also find that including the lateral view increases performance for 32 labels in the dataset, while being neutral for the others. The increase in overall performance is comparable to the one obtained by using only the PA view with twice the amount of patients in the training set.

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The forgotten view: Chest X-ray - Lateral view

Cited by 5 publications

References 9 publications

COVID-CXNet: Detecting COVID-19 in frontal chest X-ray images using deep learning

COVID-CXNet: Detecting COVID-19 in frontal chest X-ray images using deep learning

COVID-CXNet: Detecting COVID-19 in Frontal Chest X-ray Images using Deep Learning

Quantifying the Value of Lateral Views in Deep Learning for Chest X-rays

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