Deep learning for COVID-19 detection based on CT images

Zhao, Wentao; Jiang, Wei; Qiu, Xinguo

doi:10.1038/s41598-021-93832-2

Cited by 130 publications

(99 citation statements)

References 54 publications

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“…Among the most recent ones [46,47], 3D images could be handy to avoid losing the interstitial information of the lungs. However, several works have exploited 2D images showing the property of extracting representative features of COVID-19 lesions for disease detection [48][49][50][51][52][53][54][55][56]. They are all CNN-based and used CT [48][49][50][51][52][53][54][55] or CXR [43,50,56] images.…”

Section: Related Workmentioning

confidence: 99%

“…However, several works have exploited 2D images showing the property of extracting representative features of COVID-19 lesions for disease detection [48][49][50][51][52][53][54][55][56]. They are all CNN-based and used CT [48][49][50][51][52][53][54][55] or CXR [43,50,56] images. We particularly focused this study on deep learning-based classification methods for COVID-19 detection.…”

Section: Related Workmentioning

confidence: 99%

“…Biswas et al [53] used a transfer learning strategy on the three pretrained models of VGG-16 [57], ResNet50 [59], and Xception [60], combining them with the ensemble stacking strategy and tested the method on CT images of the chest. Zhao et al [55] adopted the ResNet-v2, a modified version of ResNet [59]. Moreover, they added group normalisation instead of batch normalisation and conducted a weight standardisation for all convolutional layers.…”

Section: Related Workmentioning

confidence: 99%

“…As it can be seen, the works of Gunraj et al [54] produced significant early results with their proposed COVID-Net CT convolutional neural network. However, recently, the work of Zhao et al [55] outperformed state of the art, reaching 99.2% using ResNet-v2, a modified version of ResNet and several improvements, such as the group normalisation or the weight standardisation for all convolutional layers. Finally, our baseline work demonstrates that even pre-existing architectures can reach outstanding results.…”

Section: Comparison With the State-of-the-artmentioning

confidence: 99%

See 3 more Smart Citations

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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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Comparison With the State-of-the-artmentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning for COVID-19 Diagnosis from CT Images

2021

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“…These methods train the weights of the network on large datasets and fine-tune the weights of the pretrained network using small datasets. Because only a limited amount of data is present in the current CXR datasets, the use of transfer learning is extremely important for effective COVID-19 detection [29]. With transfer learning, Apostolopoulos and Mpesiana [30] detected various abnormalities from small X-ray images; the results showed that deep learning with X-ray imaging utilizing transfer learning could successfully extract biomarkers related to the COVID-19 disease.…”

Section: Related Workmentioning

confidence: 99%

Fine-Tuning Convolutional Neural Networks for COVID-19 Detection from Chest X-ray Images

2021

Self Cite

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As the COVID-19 pandemic continues to ravage the world, the use of chest X-ray (CXR) images as a complementary screening strategy to reverse transcription-polymerase chain reaction (RT-PCR) testing continues to grow owing to its routine clinical application to respiratory diseases. We performed extensive convolutional neural network (CNN) fine-tuning experiments and identified that models pretrained on larger out-of-domain datasets show an improved performance. This suggests that a priori knowledge of models from out-of-field training should also apply to X-ray images. With appropriate hyperparameters selection, we found that higher resolution images carry more clinical information, and the use of mixup in training improved the performance of the model. The experimental showed that our proposed transfer learning present state-of-the-art results. Furthermore, we evaluated the performance of our model with a small amount of downstream training data and found that the model still performed well in COVID-19 identification. We also explored the mechanism of model detection using a gradient-weighted class activation mapping (Grad-CAM) method for CXR imaging to interpret the detection of radiology images. The results helped us understand how the model detects COVID-19, which can be used to discover new visual features and assist radiologists in screening.

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COVID‐DAI: A novel framework forCOVID‐19 detection and infection growth estimation using computed tomography images

Nazir

Nawaz

Javed

et al. 2022

Microscopy Res & Technique

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The COVID‐19 pandemic is spreading at a fast pace around the world and has a high mortality rate. Since there is no proper treatment of COVID‐19 and its multiple variants, for example, Alpha, Beta, Gamma, and Delta, being more infectious in nature are affecting millions of people, further complicates the detection process, so, victims are at the risk of death. However, timely and accurate diagnosis of this deadly virus can not only save the patients from life loss but can also prevent them from the complex treatment procedures. Accurate segmentation and classification of COVID‐19 is a tedious job due to the extensive variations in its shape and similarity with other diseases like Pneumonia. Furthermore, the existing techniques have hardly focused on the infection growth estimation over time which can assist the doctors to better analyze the condition of COVID‐19‐affected patients. In this work, we tried to overcome the shortcomings of existing studies by proposing a model capable of segmenting, classifying the COVID‐19 from computed tomography images, and predicting its behavior over a certain period. The framework comprises four main steps: (i) data preparation, (ii) segmentation, (iii) infection growth estimation, and (iv) classification. After performing the pre‐processing step, we introduced the DenseNet‐77 based UNET approach. Initially, the DenseNet‐77 is used at the Encoder module of the UNET model to calculate the deep keypoints which are later segmented to show the coronavirus region. Then, the infection growth estimation of COVID‐19 per patient is estimated using the blob analysis. Finally, we employed the DenseNet‐77 framework as an end‐to‐end network to classify the input images into three classes namely healthy, COVID‐19‐affected, and pneumonia images. We evaluated the proposed model over the COVID‐19‐20 and COVIDx CT‐2A datasets for segmentation and classification tasks, respectively. Furthermore, unlike existing techniques, we performed a cross‐dataset evaluation to show the generalization ability of our method. The quantitative and qualitative evaluation confirms that our method is robust to both COVID‐19 segmentation and classification and can accurately predict the infection growth in a certain time frame.

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Deep learning for COVID-19 detection based on CT images

Cited by 130 publications

References 54 publications

Deep Learning for COVID-19 Diagnosis from CT Images

Deep Learning for COVID-19 Diagnosis from CT Images

Fine-Tuning Convolutional Neural Networks for COVID-19 Detection from Chest X-ray Images

COVID‐DAI: A novel framework forCOVID‐19 detection and infection growth estimation using computed tomography images

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