Deep learning assisted COVID-19 detection using full CT-scans

Rohila, Varan Singh; Gupta, Nitin; Kaul, Amit; Sharma, Deepak Kumar

doi:10.1016/j.iot.2021.100377

Cited by 48 publications

(20 citation statements)

References 24 publications

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“…The COVID-19 pandemic has caused huge damage in the world since 2019. Several studies [16][17][18][19] applied deep neural networks to detect coronavirus diseases from the X-ray and computed tomography (CT) images, and some evaluated multiple convolutional neural network (CNN) models. Chen et al [20] proposed an efficient deep learning model for removing irrelevant backgrounds, extracting spatial features, and automatically segmenting lung lesions from CT images.…”

Section: Related Workmentioning

confidence: 99%

Analyzing Groups of Inpatients’ Healthcare Needs to Improve Service Quality and Sustainability

et al. 2021

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The trend towards personalized healthcare has led to an increase in applying deep learning techniques to improve healthcare service quality and sustainability. With the increasing number of patients with multiple comorbidities, they need comprehensive care services, where comprehensive care is a synonym for complete patient care to respond to a patient’s physical, emotional, social, economic, and spiritual needs, and, as such, an efficient prediction system for comprehensive care suggestions could help physicians and healthcare providers in making clinical judgement. The experiment dataset contained a total of 2.9 million electrical medical records (EMRs) from 250 thousand hospitalized patients collected retrospectively from a first-tier medical center in Taiwan, where the EMRs were de-identified and anonymized and where 949 cases had received comprehensive care. Recurrent neural networks (RNNs) are designed for analyzing time-series data but are still lacking in studying predicting personalized healthcare. Furthermore, in most cases, the collected evaluation data are imbalanced with a small portion of positive cases. This study examined the impact of imbalanced data in model training and suggested an effective approach to handle such a situation. To address the above-mentioned research issue, this study analyzed the care need in the different patient groupings, proposed a personalized care suggestion system by applying RNN models, and developed an efficient model training scheme for building AI-assisted prediction models. This study observed several findings: (1) the data resampling schemes could mitigate the impact of imbalanced data on model training, and the under-sampling scheme achieved the best performance with an ACC of 99.80%, a PPV of 70.18%, an NPV of 99.87%, a recall of 82.91%, and an F1 score of 0.7602, while the model trained with the original data had a very low PPV of 6.42% and a low F1 score of 0.1116; (2) patient clustering with multi-classier could predict comprehensive care needs efficiently with an ACC of 99.87%, a PPV of 77.90%, an NPV of 99.90%, a recall of 92.19%, and an F1 score of 0.8404; (3) the proposed long short-term memory (LSTM) prediction model achieved the best overall performance with an ACC of 99.80%, a PPV of 70.18%, an NPV of 99.87%, a recall of 82.91%, and an F1 score of 0.7602.

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Section: Related Workmentioning

confidence: 99%

Analyzing Groups of Inpatients’ Healthcare Needs to Improve Service Quality and Sustainability

et al. 2021

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“…The novel coronavirus disease 2019 (COVID-19) was first reported in December 2019 in Wuhan City, Hubei province of China [ 1 , 2 ]. Compared to the SARS-CoV epidemic in 2003, the contagiousness of COVID-19 was found to be more severe in a short period [ 2 , 3 ]. The ability of COVID-19 to be transmitted from person to person, particularly through respiratory droplets and close contact, has led to an exponential increase in the number of positive cases [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

“…Although it is the main diagnostic kit, the method is time-consuming and leads to a considerable proportion of false-negative results [ 7 , 8 ]. As an alternative to laboratory tests, chest X-ray and computed tomography (CT) scan have also proved helpful in diagnosing the disease and minimize the disadvantages of RT-PCR [ 3 ]. The fact that CT scans can be easily performed in most medical centers, have a high spatial resolution, and are more sensitive in detecting the irregularities in the lungs caused by COVID-19 encourages physicians to use this method frequently.…”

Section: Introductionmentioning

confidence: 99%

Multi-task semantic segmentation of CT images for COVID-19 infections using DeepLabV3+ based on dilated residual network

Polat

2022

Phys Eng Sci Med

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COVID-19 is a deadly outbreak that has been declared a public health emergency of international concern. The massive damage of the disease to public health, social life, and the global economy increases the importance of alternative rapid diagnosis and follow-up methods. RT-PCR assay, which is considered the gold standard in diagnosing the disease, is complicated, expensive, time-consuming, prone to contamination, and may give false-negative results. These drawbacks reinforce the trend toward medical imaging techniques such as computed tomography (CT). Typical visual signs such as ground-glass opacity (GGO) and consolidation of CT images allow for quantitative assessment of the disease. In this context, it is aimed at the segmentation of the infected lung CT images with the residual network-based DeepLabV3+, which is a redesigned convolutional neural network (CNN) model. In order to evaluate the robustness of the proposed model, three different segmentation tasks as Task-1, Task-2, and Task-3 were applied. Task-1 represents binary segmentation as lung (infected and non-infected tissues) and background. Task-2 represents multi-class segmentation as lung (non-infected tissue), COVID (GGO, consolidation, and pleural effusion irregularities are gathered under a single roof), and background. Finally, the segmentation in which each lesion type is considered as a separate class is defined as Task-3. COVID-19 imaging data for each segmentation task consists of 100 CT single-slice scans from over 40 diagnosed patients. The performance of the model was evaluated using Dice similarity coefficient (DSC), intersection over union (IoU), sensitivity, specificity, and accuracy by performing five-fold cross-validation. The average DSC performance for three different segmentation tasks was obtained as 0.98, 0.858, and 0.616, respectively. The experimental results demonstrate that the proposed method has robust performance and great potential in evaluating COVID-19 infection.

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“…Table 1 summarizes some of the key findings reported by the peer works. Some of the authors suggested different deep learning models such as ResNet-101, ResNet-50, DenseNet-169, DenseNet-201 [26], ResNet-18 [29], AlexNet [31], CNN [32], for the diagnosis of disease using CT images, CXR images but very few have suggested bi-level programming Stackelberg [33], optimizer algorithm [34] for multi-classes. For healthcare operations some authors suggested different heuristics and metaheuristics models [35], multi-depot routing model [36], logistic problems [37], and some suggested mesenchymal stem cell (MSC) therapy [38] treatment.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images

Kaur

Harnal

Tiwari

et al. 2021

IJERPH

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COVID-19 declared as a pandemic that has a faster rate of infection and has impacted the lives and the country’s economy due to forced lockdowns. Its detection using RT-PCR is required long time and due to which its infection has grown exponentially. This creates havoc for the shortage of testing kits in many countries. This work has proposed a new image processing-based technique for the health care systems named “C19D-Net”, to detect “COVID-19” infection from “Chest X-Ray” (XR) images, which can help radiologists to improve their accuracy of detection COVID-19. The proposed system extracts deep learning (DL) features by applying the InceptionV4 architecture and Multiclass SVM classifier to classify and detect COVID-19 infection into four different classes. The dataset of 1900 Chest XR images has been collected from two publicly accessible databases. Images are pre-processed with proper scaling and regular feeding to the proposed model for accuracy attainments. Extensive tests are conducted with the proposed model (“C19D-Net”) and it has succeeded to achieve the highest COVID-19 detection accuracy as 96.24% for 4-classes, 95.51% for three-classes, and 98.1% for two-classes. The proposed method has outperformed well in expressions of “precision”, “accuracy”, “F1-score” and “recall” in comparison with most of the recent previously published methods. As a result, for the present situation of COVID-19, the proposed “C19D-Net” can be employed in places where test kits are in short supply, to help the radiologists to improve their accuracy of detection of COVID-19 patients through XR-Images.

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Deep learning assisted COVID-19 detection using full CT-scans

Cited by 48 publications

References 24 publications

Analyzing Groups of Inpatients’ Healthcare Needs to Improve Service Quality and Sustainability

Analyzing Groups of Inpatients’ Healthcare Needs to Improve Service Quality and Sustainability

Multi-task semantic segmentation of CT images for COVID-19 infections using DeepLabV3+ based on dilated residual network

A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images

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