Deep learning for chest X-ray analysis: A survey

Çallı, Erdi; Sogancioglu, Ecem; Ginneken, Bram van; Leeuwen, Kicky G. van; Murphy, Keelin

doi:10.1016/j.media.2021.102125

Cited by 285 publications

(149 citation statements)

References 193 publications

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“…Refs. [ [12] , [13] , [14] ]). On the other hand, X-ray images are stored and transmitted in the form of compressed data [ 32 ].…”

Section: Datamentioning

confidence: 99%

“…It is reportedly used for COVID-19 detection in countries with a shortage of testing kits [ [6] , [7] , [8] ]. Recent studies [ [9] , [10] , [11] , [12] , [13] , [14] ] using machine learning (ML) and deep learning (DL) have shown promising results in the diagnosis of COVID-19. For example, convolutional neural networks (CNNs) have been applied for the classification of X-ray images [ [9] , [10] , [11] ] among COVID-19, non-COVID pneumonia (e.g., bacterial and viral pneumonia) and healthy cases.…”

Section: Introductionmentioning

confidence: 99%

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Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of COVID-19 in X-ray images

Momeny

Neshat

Hussain

et al. 2021

Computers in Biology and Medicine

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Chest X-ray images are used in deep convolutional neural networks for the detection of COVID-19, the greatest human challenge of the 21st century. Robustness to noise and improvement of generalization are the major challenges in designing these networks. In this paper, we introduce a strategy for data augmentation using the determination of the type and value of noise density to improve the robustness and generalization of deep CNNs for COVID-19 detection. Firstly, we present a learning-to-augment approach that generates new noisy variants of the original image data with optimized noise density. We apply a Bayesian optimization technique to control and choose the optimal noise type and its parameters. Secondly, we propose a novel data augmentation strategy, based on denoised X-ray images, that uses the distance between denoised and original pixels to generate new data. We develop an autoencoder model to create new data using denoised images corrupted by the Gaussian and impulse noise. A database of chest X-ray images, containing COVID-19 positive, healthy, and non-COVID pneumonia cases, is used to fine-tune the pre-trained networks (AlexNet, ShuffleNet, ResNet18, and GoogleNet). The proposed method performs better results compared to the state-of-the-art learning to augment strategies in terms of sensitivity (0.808), specificity (0.915), and F-Measure (0.737). The source code of the proposed method is available at https://github.com/mohamadmomeny/Learning-to-augment-strategy .

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“…Refs. [ [12] , [13] , [14] ]). On the other hand, X-ray images are stored and transmitted in the form of compressed data [ 32 ].…”

Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of COVID-19 in X-ray images

Momeny

Neshat

Hussain

et al. 2021

Computers in Biology and Medicine

View full text Add to dashboard Cite

show abstract

“…A GAN can be used to bridge the domain gap between training and external data from different sources [ 64 ]. If difficult access to reliable annotated data from multiple data sources remains problematic, domain adaptation can be considered to address the generalization issue [ 87 ]. Domain adaptation via the domain transfer function of a GAN may provide a chance to use a machine learning system in different settings.…”

Section: Discussionmentioning

confidence: 99%

Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey

et al. 2022

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Background Recent advances in deep learning techniques have led to improved diagnostic abilities in ophthalmology. A generative adversarial network (GAN), which consists of two competing types of deep neural networks, including a generator and a discriminator, has demonstrated remarkable performance in image synthesis and image-to-image translation. The adoption of GAN for medical imaging is increasing for image generation and translation, but it is not familiar to researchers in the field of ophthalmology. In this work, we present a literature review on the application of GAN in ophthalmology image domains to discuss important contributions and to identify potential future research directions. Methods We performed a survey on studies using GAN published before June 2021 only, and we introduced various applications of GAN in ophthalmology image domains. The search identified 48 peer-reviewed papers in the final review. The type of GAN used in the analysis, task, imaging domain, and the outcome were collected to verify the usefulness of the GAN. Results In ophthalmology image domains, GAN can perform segmentation, data augmentation, denoising, domain transfer, super-resolution, post-intervention prediction, and feature extraction. GAN techniques have established an extension of datasets and modalities in ophthalmology. GAN has several limitations, such as mode collapse, spatial deformities, unintended changes, and the generation of high-frequency noises and artifacts of checkerboard patterns. Conclusions The use of GAN has benefited the various tasks in ophthalmology image domains. Based on our observations, the adoption of GAN in ophthalmology is still in a very early stage of clinical validation compared with deep learning classification techniques because several problems need to be overcome for practical use. However, the proper selection of the GAN technique and statistical modeling of ocular imaging will greatly improve the performance of each image analysis. Finally, this survey would enable researchers to access the appropriate GAN technique to maximize the potential of ophthalmology datasets for deep learning research.

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“…Therefore, they differ from conventional machine learning methods because they require little or no image preprocessing and can automatically infer an optimal data representation from raw images without requiring prior feature selection, resulting in a more objective and less biased process. Furthermore, they achieved optimal results in many domains, such as computer vision devoted to medical analysis, with images coming from magnetic resonance imaging (MRI) [27], microscopy [28], CT [29], ultrasound [30], X-ray [31], and mammography [32]. They have been successfully applied to various different problems, like classification or segmentation [33][34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for COVID-19 Diagnosis from CT Images

2021

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COVID-19, an infectious coronavirus disease, caused a pandemic with countless deaths. From the outset, clinical institutes have explored computed tomography as an effective and complementary screening tool alongside the reverse transcriptase-polymerase chain reaction. Deep learning techniques have shown promising results in similar medical tasks and, hence, may provide solutions to COVID-19 based on medical images of patients. We aim to contribute to the research in this field by: (i) Comparing different architectures on a public and extended reference dataset to find the most suitable; (ii) Proposing a patient-oriented investigation of the best performing networks; and (iii) Evaluating their robustness in a real-world scenario, represented by cross-dataset experiments. We exploited ten well-known convolutional neural networks on two public datasets. The results show that, on the reference dataset, the most suitable architecture is VGG19, which (i) Achieved 98.87% accuracy in the network comparison; (ii) Obtained 95.91% accuracy on the patient status classification, even though it misclassifies some patients that other networks classify correctly; and (iii) The cross-dataset experiments exhibit the limitations of deep learning approaches in a real-world scenario with 70.15% accuracy, which need further investigation to improve the robustness. Thus, VGG19 architecture showed promising performance in the classification of COVID-19 cases. Nonetheless, this architecture enables extensive improvements based on its modification, or even with preprocessing step in addition to it. Finally, the cross-dataset experiments exposed the critical weakness of classifying images from heterogeneous data sources, compatible with a real-world scenario.

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Deep learning for chest X-ray analysis: A survey

Cited by 285 publications

References 193 publications

Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of COVID-19 in X-ray images

Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of COVID-19 in X-ray images

Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey

Deep Learning for COVID-19 Diagnosis from CT Images

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