Classification of Abnormality in Chest X-Ray Images by Transfer Learning of CheXNet

Almuhayar, Mawanda; Lu, Henry Horng-Shing; Iriawan, Nur

doi:10.1109/icicos48119.2019.8982455

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…For each image, to get the bottleneck features is mainly the goal here. In both approaches we used different layers of CheXNet [6] for output but we have taken ReLU (avg_pooling) layer as output in our first approach CNN-RNN. We rescaled every CXR to (224,224,3) and then delivered it to the CheXNet through which we obtained 1024 size feature vector.…”

Section: Resultsmentioning

confidence: 99%

“…This model contains the branch of classification that determines the image representations and an attention branch works for acquiring distinguished attention maps for better performance of classification. We have used an attention branch, CheXNet [6] is used as classification branch then outputs of mutual branches are aggregated for identification of individual input. Conversely, an automatic radiological diagnosis and reporting system like this system is a crucial framework to build.…”

Section: Figure 1 Representation Of Normal and Abnormal Chest Radiogr...mentioning

confidence: 99%

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Report Generation of Lungs Diseases From Chest X-ray using NLP

Iftikhar

Naz

Zahra

et al. 2022

IJIST

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Pulmonary diseases are very severe health complications in the world that impose a massive worldwide health burden. These diseases comprise of pneumonia, asthma, tuberculosis, Covid-19, cancer, etc. The evidences show that around 65 million people undergo the chronic obstructive pulmonary disease and nearly 3 million people pass away from it each year that make it the third prominent reason of death worldwide. To decrease the burden of lungs diseases timely diagnosis is very essential. Computer-aided diagnostic, are systems that support doctors in the analysis of medical images. This study showcases that Report Generation System has automated the Chest X-Ray interpretation procedure and lessen human effort, consequently helped the people for timely diagnoses of chronic lungs diseases to decrease the death rate. This system provides great relief for people in rural areas where the doctor-to-patient ratio is only 1 doctor per 1300 people. As a result, after utilizing this application, the affected individual can seek further therapy for the ailment they have been diagnosed with. The proposed system is supposed to be used in the distinct architecture of deep learning (Deep Convolution Neural Network), this is fine tuned to CNN-RNN trainable end-to-end architecture. By using the patient-wise official split of the OpenI dataset we have trained a CNN-RNN model with attention. Our model achieved an accuracy of 94%, which is the highest performance.

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Section: Resultsmentioning

confidence: 99%

Section: Figure 1 Representation Of Normal and Abnormal Chest Radiogr...mentioning

confidence: 99%

Report Generation of Lungs Diseases From Chest X-ray using NLP

Iftikhar

Naz

Zahra

et al. 2022

IJIST

View full text Add to dashboard Cite

show abstract

“…CheXNet: CheXNet is trained on a very large dataset of CXRs and has been used for transfer learning by some other thoracic disease identification studies [99,100]. However, it has its own deficiencies, such as individual sample variability as a result of data ordering changes [101] and vulnerability to adversarial attacks [102].…”

Section: Architecturementioning

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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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.

show abstract

Classification of Abnormality in Chest X-Ray Images by Transfer Learning of CheXNet

Cited by 5 publications

References 9 publications

Report Generation of Lungs Diseases From Chest X-ray using NLP

Report Generation of Lungs Diseases From Chest X-ray using NLP

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

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