Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification

Baltruschat, Ivo M.; Nickisch, Hannes; Graß, Michael; Knopp, Tobias; Saalbach, Axel

doi:10.1038/s41598-019-42294-8

Cited by 351 publications

(283 citation statements)

References 21 publications

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“…due to a somewhat fuzzy diagnostic boundary between infiltrations and pneumonia). Indeed, the quality of the ground truth labelling of the dataset has been criticized before [28,29] and might be one of the main reasons for the reduced performance of training purely on the artificially generated radiographs. Another main reason is the limited size of the database of real radiographs that is available for training.…”

Section: Performance Of Classifiers Augmented With Anonymous Radiographsmentioning

confidence: 99%

Breaking Medical Data Sharing Boundaries by Employing Artificial Radiographs

Han

Nebelung

Haarburger

et al. 2019

Preprint

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Artificial intelligence (AI) has the potential to change medicine fundamentally. Here, expert knowledge provided by AI can enhance diagnosis by comprehensive and user independent integration of multiple image features. Unfortunately, existing algorithms often stay behind expectations, as databases used for training are usually too small, incomplete, and heterogeneous in quality. Additionally, data protection constitutes a serious obstacle to data sharing. We propose to use generative models (GM) to produce high-resolution artificial radiographs, which are free of personal identifying information. Blinded analyses by computer vision and radiology experts proved the high similarity of artificial and real radiographs. The combination of multiple GM improves the performance of computer vision algorithms and the integration of artificial data into patient data repositories can compensate for underrepresented disease entities. Furthermore, the low computational effort of our method complies with existing IT infrastructure in hospitals and thus facilitates its dissemination. We envision that our approach could lead to scalable databases of anonymous medical images enabling standardized radiomic analyses at multiple sites.

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Section: Performance Of Classifiers Augmented With Anonymous Radiographsmentioning

confidence: 99%

Breaking Medical Data Sharing Boundaries by Employing Artificial Radiographs

Han

Nebelung

Haarburger

et al. 2019

Preprint

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“…In recently, several classical image processing and machine or deep learning methods are used to automatically classify the diseases with digitized chest X-ray images 13,14 . Class decomposition of the coronavirus disease-2019 (COVID-19) in non-COVID and COVID viral infection with X-ray scans regarded as one of the critical subjects of matter for diagnosing this highly infectious disease [15][16][17] .…”

Section: Introductionmentioning

confidence: 99%

Accurate Prediction of COVID-19 using Chest X-Ray Images through Deep Feature Learning model with SMOTE and Machine Learning Classifiers

Kumar

Arora

Bansal

et al. 2020

Preprint

116

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According to the World Health Organization (WHO), the coronavirus (COVID-19) pandemic is putting even the best healthcare systems across the world under tremendous pressure. The early detection of this type of virus will help in relieving the pressure of the healthcare systems. Chest X-rays has been playing a crucial role in the diagnosis of diseases like Pneumonia. As COVID-19 is a type of influenza, it is possible to diagnose using this imaging technique. With rapid development in the area of Machine Learning (ML) and Deep learning, there had been intelligent systems to classify between Pneumonia and Normal patients. This paper proposes the machine learning-based classification of the extracted deep feature using ResNet152 with COVID-19 and Pneumonia patients on chest X-ray images. SMOTE is used for balancing the imbalanced data points of COVID-19 and Normal patients. This non-invasive and early prediction of novel coronavirus (COVID-19) by analyzing chest X-rays can further be used to predict the spread of the virus in asymptomatic patients. The model is achieving an accuracy of 0.973 on Random Forest and 0.977 using XGBoost predictive classifiers. The establishment of such an approach will be useful to predict the outbreak early, which in turn can aid to control it effectively.

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“…The work took the relevance of disease into network and improved the accuracy. Baltruschat et al . integrated non‐image features into ResNet‐50 to identify diseases, the non‐image features include patient age, gender and view position.…”

Section: Introductionmentioning

confidence: 99%

“…The work 18 took the relevance of disease into network and improved the accuracy. Baltruschat et al 20 integrated non-image features into ResNet-50 21 to identify diseases, the non-image features include patient age, gender and view position. They hold the view that the occurrence of disease is related to the age and gender of patient, so the identification accuracy can be improved by introducing these non-image features.…”

Section: Introductionmentioning

confidence: 99%

Dense networks with relative location awareness for thorax disease identification

et al. 2019

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Purpose Chest X‐ray is one of the most common examinations for diagnosing heart and lung diseases. Due to the existing of a large number of clinical cases, many automated diagnosis algorithms based on chest X‐ray images have been proposed. To our knowledge, almost none of the previous auto‐diagnosis algorithms consider the effect of relative location information on disease incidence. In this study, we propose to use relative location information to assist the identification of thorax diseases. Method In this work, U‐Net is used to segment lung and heart from chest image. The relative location maps are computed through Euclidean distance transformation from segmented masks. By introducing the relative location information into the network, the usual location of disease is combined with the incidence. The proposed network is the fusion of two branches: mask branch and image branch. A mask branch is designed to be a bottom‐up and top‐down structure to extract relative location information. The structure has a large receptive field, which can extract more information for large lesion and contextual information for small lesion. The features learned from mask branch are fused with image branch, which is a 121‐layers DenseNet. Results We compare our proposed method with four state‐of‐the‐art methods on the largest public chest X‐ray dataset: ChestX‐ray14. The proposed method achieves the area under a curve of 0.820, which outperforms all the existing models and algorithms. Conclusion This paper proposed a dense network with relative location information to identify thorax disease. The method combines the usual location of disease with the incidence for the first time and performs good.

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Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification

Cited by 351 publications

References 21 publications

Breaking Medical Data Sharing Boundaries by Employing Artificial Radiographs

Breaking Medical Data Sharing Boundaries by Employing Artificial Radiographs

Accurate Prediction of COVID-19 using Chest X-Ray Images through Deep Feature Learning model with SMOTE and Machine Learning Classifiers

Dense networks with relative location awareness for thorax disease identification

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