A Novel Approach for Multi-Label Chest X-Ray Classification of Common Thorax Diseases

Allaouzi, Imane; Mohamed, Ben Ahmed

doi:10.1109/access.2019.2916849

Cited by 87 publications

(43 citation statements)

References 17 publications

(19 reference statements)

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“…Implementing a human-in-the loop approach for partially correcting the label noise could further improve performance of networks trained on the CheXpert dataset 21 . Our findings differ from applied techniques used in previous literature, where deeper network architectures, mainly a DenseNet-121, were used to classify the CheXpert data set 6,9,22 . The authors of the CheXpert dataset achieved an average overall AUROC of 0.889 3 , using a DenseNet-121, which was not surpassed by any of the models used in our analysis, although differences between the best performing networks and the CheXpert baseline were smaller than 0.01.…”

Section: Discussioncontrasting

confidence: 99%

“…The good results are probably due to the hierarchical structure of the classification framework, which takes into account correlations between different labels, and the application of a label-smoothing technique, which also allows the use of uncertainty labels (which were excluded in our present work). Allaouzi et al similarly used a DenseNet-121 and created three different models for the classification of the CheXpert and ChestX-ray14, yielding an AUC of 0.72 for atelectasis, 0.87-0.88 for cardiomegaly, 0.74-0.77 for consolidation, 0.86-0.87 for edema and 0.90 for effusion 22 . Except for cardiomegaly, we achieved better values with several models (e.g.…”

Section: Discussionmentioning

confidence: 99%

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Comparing different deep learning architectures for classification of chest radiographs

Bressem

Adams

Erxleben

et al. 2020

Sci Rep

140

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Chest radiographs are among the most frequently acquired images in radiology and are often the subject of computer vision research. However, most of the models used to classify chest radiographs are derived from openly available deep neural networks, trained on large image datasets. These datasets differ from chest radiographs in that they are mostly color images and have substantially more labels. Therefore, very deep convolutional neural networks (CNN) designed for ImageNet and often representing more complex relationships, might not be required for the comparably simpler task of classifying medical image data. Sixteen different architectures of CNN were compared regarding the classification performance on two openly available datasets, the CheXpert and COVID-19 Image Data Collection. Areas under the receiver operating characteristics curves (AUROC) between 0.83 and 0.89 could be achieved on the CheXpert dataset. On the COVID-19 Image Data Collection, all models showed an excellent ability to detect COVID-19 and non-COVID pneumonia with AUROC values between 0.983 and 0.998. It could be observed, that more shallow networks may achieve results comparable to their deeper and more complex counterparts with shorter training times, enabling classification performances on medical image data close to the state-of-the-art methods even when using limited hardware. Chest radiographs are among the most frequently used imaging procedures in radiology. They have been widely employed in the field of computer vision, as chest radiographs are a standardized technique and, if compared to other radiological examinations such as computed tomography or magnetic resonance imaging, contain a smaller group of relevant pathologies. Although many artificial neural networks for the classification of chest radiographs have been developed, it is still subject to intensive research. Only a few groups design their own networks from scratch, while most use already established architectures, such as ResNet-50 or DenseNet-121 (with 50 and 121 representing the number of layers within the respective neural network) 1-6. These neural networks have often been trained on large, openly available datasets, such as ImageNet, and are therefore already able to recognize numerous image features. When training a model for a new task, such as the classification of chest radiographs, the use of pre-trained networks may improve the training speed and accuracy of the new model, since important image features that have already been learned can be transferred to the new task and do not have to be learned again. However, the feature space of freely available data sets such as ImageNet differs from chest radiographs as they contain color images and more categories. The ImageNet Challenge includes 1,000 possible categories per image, while CheXpert, a large freely available data set of chest radiographs, only distinguishes between 14 categories (or classes) 7 , and the COVID-19 Image Data Collection only differentiates between three classes 8. Although the ImageNet...

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Section: Discussioncontrasting

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Comparing different deep learning architectures for classification of chest radiographs

Bressem

Adams

Erxleben

et al. 2020

Sci Rep

140

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“…Allaouzi and Ahmed, presented a novel CNN [31] that achieved better performance in both, CX14 and CheXpert datasets, surpassing the baseline established by Irving et al…”

Section: Transfer Learning and Chest Diseasesmentioning

confidence: 93%

A Transfer Learning Method for Pneumonia Classification and Visualization

et al. 2020

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Pneumonia is an infectious disease that affects the lungs and is one of the principal causes of death in children under five years old. The Chest X-ray images technique is one of the most used for diagnosing pneumonia. Several Machine Learning algorithms have been successfully used in order to provide computer-aided diagnosis by automatic classification of medical images. For its remarkable results, the Convolutional Neural Networks (models based on Deep Learning) that are widely used in Computer Vision tasks, such as classification of injuries and brain abnormalities, among others, stand out. In this paper, we present a transfer learning method that automatically classifies between 3883 chest X-ray images characterized as depicting pneumonia and 1349 labeled as normal. The proposed method uses the Xception Network pre-trained weights on ImageNet as an initialization. Our model is competitive with respect to state-of-the-art proposals. To make comparisons with other models, we have used four well-known performance measures, obtaining the following results: precision (0.84), recall (0.99), F1-score (0.91) and area under the ROC curve (0.97). These positive results allow us to consider our proposal as an alternative that can be useful in countries with a lack of equipment and specialized radiologists.

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“…The CX14 contains posterior-anterior images from the chest, with a total of 108,948 images. The CX14 represented a big milestone for CAD, CV, and DL applications for multiple chest diseases classification [13,23,24].…”

Section: Convolutional Neural Network and Chest Diseasesmentioning

confidence: 99%

“…One of the best current methods for computer vision (CV) tasks are the convolutional neural networks (CNN) [7]. A lot of techniques have been used in the medical field to assist CAD tasks, such as lesion segmentation [8], brain tumor segmentation [9,10], automatic size calculation of the heart [11], and classification among several thorax diseases [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Fast COVID-19 and Pneumonia Classification Using Chest X-ray Images

et al. 2020

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As of the end of 2019, the world suffered from a disease caused by the SARS-CoV-2 virus, which has become the pandemic COVID-19. This aggressive disease deteriorates the human respiratory system. Patients with COVID-19 can develop symptoms that belong to the common flu, pneumonia, and other respiratory diseases in the first four to ten days after they have been infected. As a result, it can cause misdiagnosis between patients with COVID-19 and typical pneumonia. Some deep-learning techniques can help physicians to obtain an effective pre-diagnosis. The content of this article consists of a deep-learning model, specifically a convolutional neural network with pre-trained weights, which allows us to use transfer learning to obtain new retrained models to classify COVID-19, pneumonia, and healthy patients. One of the main findings of this article is that the following relevant result was obtained in the dataset that we used for the experiments: all the patients infected with SARS-CoV-2 and all the patients infected with pneumonia were correctly classified. These results allow us to conclude that the proposed method in this article may be useful to help physicians decide the diagnoses related to COVID-19 and typical pneumonia.

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A Novel Approach for Multi-Label Chest X-Ray Classification of Common Thorax Diseases

Cited by 87 publications

References 17 publications

Comparing different deep learning architectures for classification of chest radiographs

Comparing different deep learning architectures for classification of chest radiographs

A Transfer Learning Method for Pneumonia Classification and Visualization

Fast COVID-19 and Pneumonia Classification Using Chest X-ray Images

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