CNR-IEMN: A Deep Learning Based Approach to Recognise Covid-19 from CT-Scan

Bougourzi, Fares; Contino, Riccardo; Distante, Cosimo; Taleb‐Ahmed, Abdelmalik

doi:10.1109/icassp39728.2021.9414185

Cited by 27 publications

(16 citation statements)

References 14 publications

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“…We have compared our proposed framework with top six models [10][11][12][13][14][15] developed following the Signal Processing Grand Challenge (SPGC) on COVID-19 diagnosis, which was organized by the authors as part of the 2021 IEEE International Conference on Acoustics, Speech, & Signal Processing (ICASSP). In the first phase of this SPGC, participants had access to the same train and validation sets as those used in this study to develop and evaluate their models.…”

Section: Comparisonmentioning

confidence: 99%

“…• Ref. 14 : This model utilizes a two-stage framework in which the first stage is responsible for performing a multi-task classification to classify 2D slices into one of the target groups and identify the location of the slice in the sequence of CT images at the same time. The model at the first stage uses an ensemble of four popular CNN-based classifiers (i.e., ResneXt50 19 , DenseNet161 20 , Inception-V3 21 , and Wide-Resnet 22 ), followed by an aggregation mechanism that divides the whole volumetric CT scan into 20 groups of slices and calculates the percentage of infected slices related to COVID-19 and CAP classes in each group.…”

Section: Comparisonmentioning

confidence: 99%

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Robust Automated Framework for COVID-19 Disease Identification from a Multicenter Dataset of Chest CT Scans

Khademi¹,

Heidarian²,

Afshar³

et al. 2021

Preprint

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The objective of this study is to develop a robust deep learning-based framework to distinguish COVID-19, Community-Acquired Pneumonia (CAP), and Normal cases based on chest CT scans, which are acquired in different imaging centers using various scanners, scanning protocols, and radiation doses. We showed that while our proposed model is trained on a relatively small dataset acquired from only one imaging center using a specific scanning protocol, the model performs well on heterogeneous test sets obtained by multiple scanners using different technical parameters. We also showed that the model can be updated via an unsupervised approach to cope with the data shift between the train and test sets and enhance the robustness of the model upon receiving a new external dataset from a different center. More specifically, we extracted the subset of the test images for which the model generated a confident prediction and used the extracted subset along with the training set to re-train and update the benchmark model (the model trained on the initial train set). Finally, we adopted an ensemble architecture to aggregate the predictions from multiple versions of the model. For initial training and development purposes, an in-house dataset of 171 COVID-19, 60 CAP, and 76 Normal cases was used, which contained volumetric CT scans acquired from one imaging center using a constant standard radiation dose scanning protocol. To evaluate the model, we collected four different test sets retrospectively to investigate the effects of the shifts in the data characteristics on the model's performance. Among the test cases, there were CT scans with similar characteristics as the train set as well as noisy low-dose and ultra-low dose CT scans. In addition, some test CT scans were obtained from patients with a history of cardiovascular diseases or surgeries. The entire test dataset used in this study contained 51 COVID-19, 28 CAP, and 51 Normal cases. Experimental results indicate that our proposed framework performs well on all test sets achieving the a total accuracy of 96.15% (95%CI : [91.25 − 98.74]), COVID-19 sensitivity of 96.08% (95%CI : [86.54 − 99.5]), CAP sensitivity of 92.86% (95%CI : [76.50 − 99.19]), Normal sensitivity of 98.04% (95%CI : [89.55 − 99.95]) while the confidence intervals are obtained using the significance level of 0.05. The obtained AUC values (One class vs Others) are 0.993 (95%CI : [0.977 − 1]), 0.989 (95%CI : [0.962 − 1]), and 0.990 (95%CI : [0.971 − 1]) for COVID-19, CAP, and Normal classes, respectively. The experimental results also demonstrate the capability of the proposed unsupervised enhancement approach to improve the performance and robustness of the model when being evaluated on varied external test sets.

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Section: Comparisonmentioning

confidence: 99%

Section: Comparisonmentioning

confidence: 99%

Robust Automated Framework for COVID-19 Disease Identification from a Multicenter Dataset of Chest CT Scans

Khademi¹,

Heidarian²,

Afshar³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…This paper is an extended version for our IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) paper [6] for the 2021 COVID-19 SPGC challenge [7]. The objective of this work is to give more details about each step of our proposed approach.…”

Section: Introductionmentioning

confidence: 99%

“…Validation of slice-level classification results using multi-tasks learning with four backbone CNN architectures (ResneXt-50, Densenet-161, Inception-v3, and Wide-Resnet-50)[6].…”

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confidence: 99%

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Recognition of COVID-19 from CT Scans Using Two-Stage Deep-Learning-Based Approach: CNR-IEMN

Bougourzi

Contino

Distante

et al. 2021

Sensors

Self Cite

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Since the appearance of the COVID-19 pandemic (at the end of 2019, Wuhan, China), the recognition of COVID-19 with medical imaging has become an active research topic for the machine learning and computer vision community. This paper is based on the results obtained from the 2021 COVID-19 SPGC challenge, which aims to classify volumetric CT scans into normal, COVID-19, or community-acquired pneumonia (Cap) classes. To this end, we proposed a deep-learning-based approach (CNR-IEMN) that consists of two main stages. In the first stage, we trained four deep learning architectures with a multi-tasks strategy for slice-level classification. In the second stage, we used the previously trained models with an XG-boost classifier to classify the whole CT scan into normal, COVID-19, or Cap classes. Our approach achieved a good result on the validation set, with an overall accuracy of 87.75% and 96.36%, 52.63%, and 95.83% sensitivities for COVID-19, Cap, and normal, respectively. On the other hand, our approach achieved fifth place on the three test datasets of SPGC in the COVID-19 challenge, where our approach achieved the best result for COVID-19 sensitivity. In addition, our approach achieved second place on two of the three testing sets.

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An Investigation of COVID-19 Diagnosis and Severity Detection Using Convolutional Neural Networks

Dhanya

Mathi

2022

Third International Conference on Image Processing and Capsule Networks

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CNR-IEMN: A Deep Learning Based Approach to Recognise Covid-19 from CT-Scan

Cited by 27 publications

References 14 publications

Robust Automated Framework for COVID-19 Disease Identification from a Multicenter Dataset of Chest CT Scans

Robust Automated Framework for COVID-19 Disease Identification from a Multicenter Dataset of Chest CT Scans

Recognition of COVID-19 from CT Scans Using Two-Stage Deep-Learning-Based Approach: CNR-IEMN

An Investigation of COVID-19 Diagnosis and Severity Detection Using Convolutional Neural Networks

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