Prediction of COVID-19 from Chest CT Images Using an Ensemble of Deep Learning Models

Biswas, Shreya; Chatterjee, Somnath; Majee, Arindam; Sen, Shibaprasad; Schwenker, Friedhelm; Sarkar, Ram

doi:10.3390/app11157004

Cited by 44 publications

(33 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This section provides a comprehensive review of existing COVID-19 detection models. Biswas et al [ 9 ] aimed to determine a strong COVID-19 predictive method via chest CT images through effective TL methods. At first, they utilized three typical DL algorithms, such as Xception, VGG-16, and ResNet50, for COVID-19 prediction.…”

Section: Related Workmentioning

confidence: 99%

Optimal Deep-Learning-Enabled Intelligent Decision Support System for SARS-CoV-2 Classification

Dutta¹,

Aljarallah

Abirami³

et al. 2022

Journal of Healthcare Engineering

View full text Add to dashboard Cite

Intelligent decision support systems (IDSS) for complex healthcare applications aim to examine a large quantity of complex healthcare data to assist doctors, researchers, pathologists, and other healthcare professionals. A decision support system (DSS) is an intelligent system that provides improved assistance in various stages of health-related disease diagnosis. At the same time, the SARS-CoV-2 infection that causes COVID-19 disease has spread globally from the beginning of 2020. Several research works reported that the imaging pattern based on computed tomography (CT) can be utilized to detect SARS-CoV-2. Earlier identification and detection of the diseases is essential to offer adequate treatment and avoid the severity of the disease. With this motivation, this study develops an efficient deep-learning-based fusion model with swarm intelligence (EDLFM-SI) for SARS-CoV-2 identification. The proposed EDLFM-SI technique aims to detect and classify the SARS-CoV-2 infection or not. Also, the EDLFM-SI technique comprises various processes, namely, data augmentation, preprocessing, feature extraction, and classification. Moreover, a fusion of capsule network (CapsNet) and MobileNet based feature extractors are employed. Besides, a water strider algorithm (WSA) is applied to fine-tune the hyperparameters involved in the DL models. Finally, a cascaded neural network (CNN) classifier is applied for detecting the existence of SARS-CoV-2. In order to showcase the improved performance of the EDLFM-SI technique, a wide range of simulations take place on the COVID-19 CT data set and the SARS-CoV-2 CT scan data set. The simulation outcomes highlighted the supremacy of the EDLFM-SI technique over the recent approaches.

show abstract

Section: Related Workmentioning

confidence: 99%

Optimal Deep-Learning-Enabled Intelligent Decision Support System for SARS-CoV-2 Classification

Dutta¹,

Aljarallah

Abirami³

et al. 2022

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…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%

“…Polsinelli et al [52] implemented a lightweight CNN, based on the SqueezeNet model [58] for efficient discrimination of COVID-19 CT images against other community-acquired pneumonia or healthy CT images. 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].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning for COVID-19 Diagnosis from CT Images

2021

View full text Add to dashboard Cite

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.

show abstract

“…Nowadays, areas such as natural language processing [7], healthcare [8][9][10], computer vision [11], autonomous driving [12], among others, have embraced machine learning (ML) DL-based models in order to reach better performance. For example, in healthcare, DL models are used for diagnosing and predicting diseases [8][9][10][13][14][15][16][17]. In prognostics and health management (PHM), much like in healthcare, ML and DL models have been used for diagnostics [18][19][20][21][22][23][24][25], prognostics [26][27][28][29][30][31][32] and anomaly detection [33,34] in machinery, showing promising results.…”

Section: Introductionmentioning

confidence: 99%

Towards Interpretable Deep Learning: A Feature Selection Framework for Prognostics and Health Management Using Deep Neural Networks

Barraza

Droguett

Martins

2021

Sensors

View full text Add to dashboard Cite

In the last five years, the inclusion of Deep Learning algorithms in prognostics and health management (PHM) has led to a performance increase in diagnostics, prognostics, and anomaly detection. However, the lack of interpretability of these models results in resistance towards their deployment. Deep Learning-based models fall within the accuracy/interpretability tradeoff, which means that their complexity leads to high performance levels but lacks interpretability. This work aims at addressing this tradeoff by proposing a technique for feature selection embedded in deep neural networks that uses a feature selection (FS) layer trained with the rest of the network to evaluate the input features’ importance. The importance values are used to determine which will be considered for deployment of a PHM model. For comparison with other techniques, this paper introduces a new metric called ranking quality score (RQS), that measures how performance evolves while following the corresponding ranking. The proposed framework is exemplified with three case studies involving health state diagnostics and prognostics and remaining useful life prediction. Results show that the proposed technique achieves higher RQS than the compared techniques, while maintaining the same performance level when compared to the same model but without an FS layer.

show abstract

Prediction of COVID-19 from Chest CT Images Using an Ensemble of Deep Learning Models

Cited by 44 publications

References 52 publications

Optimal Deep-Learning-Enabled Intelligent Decision Support System for SARS-CoV-2 Classification

Optimal Deep-Learning-Enabled Intelligent Decision Support System for SARS-CoV-2 Classification

Deep Learning for COVID-19 Diagnosis from CT Images

Towards Interpretable Deep Learning: A Feature Selection Framework for Prognostics and Health Management Using Deep Neural Networks

Contact Info

Product

Resources

About