CNR-IEMN-CD and CNR-IEMN-CSD Approaches for Covid-19 Detection and Covid-19 Severity Detection from 3D CT-scans

Since its appearance in late 2019, Covid-19 has become an active research topic for the artificial intelligence (AI) community. One of the most interesting AI topics is Covid-19 analysis from medical imaging. CT-scan imaging is the most informative tool about this disease. This work is part of the 2nd COV19D competition, where two challenges are set: Covid-19 Detection and Covid-19 Severity Detection from the CT-scans. For Covid-19 detection from CT-scans, we proposed an ensemble of 2D Convolution blocks with Densenet-161 models (CNR-IEMN-CD). Here, each 2D convolutional block with Densenet-161 architecture is trained separately and in the testing phase, the ensemble model is based on the average of their probabilities. On the other hand, we proposed an ensemble of Convolutional Layers with Inception models for Covid-19 severity detection CNR-IEMN-CSD. In addition to the Convolutional Layers, three Inception variants were used, namely Inception-v3, Inception-v4 and Inception-Resnet. Our proposed approaches outperformed the baseline approach in the validation data of the 2nd COV19D competition by 11% and 16% for Covid-19 detection and Covid-19 severity detection, respectively. In the testing phase, our proposed approach CNR-IEMN-CD ranked fifth and improved the baseline results by 18.37%. On the other hand, our proposed approach CNR-IEMN-CSD ranked third in the test data of the 2nd COV19D competition for Covid-19 severity detection, and improved the baseline results by 6.81%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

ILC-Unet plus plus for Covid-19 Infection Segmentation

Since the appearance of Covid-19 pandemic, in the end of 2019, Medical Imaging has been widely used to analysis this disease. In fact, CT-scans of the Lung can help to diagnosis, detect and quantify Covid-19 infection. In this paper, we address the segmentation of Covid-19 infection from CT-scans. In more details, we propose a CNN-based segmentation architecture named ILC-Unet++. The proposed ILC-Unet++ architecture, which is trained for both Covid-19 Infection and Lung Segmentation. The proposed architecture were tested using three datasets with two scenarios (intra and cross datasets). The experimental results showed that the proposed architecture performs better than three baseline segmentation architectures (Unet, Unet++ and Attention-Unet) and two Covid-19 infection segmentation architectures (SCOATNet and nCoVSegNet).

ILC-Unet++ for Covid-19 Infection Segmentation

Since the appearance of Covid-19 pandemic, in the end of 2019, Medical Imaging has been widely used to analysis this disease. In fact, CT-scans of the Lung can help to diagnosis, detect and quantify Covid-19 infection. In this paper, we address the segmentation of Covid-19 infection from CT-scans. In more details, we propose a CNN-based segmentation architecture named ILC-Unet++. The proposed ILC-Unet++ architecture, which is trained for both Covid-19 Infection and Lung Segmentation. The proposed architecture were tested using three datasets with two scenarios (intra and cross datasets). The experimental results showed that the proposed architecture performs better than three baseline segmentation architectures (Unet, Unet++ and Attention-Unet) and two Covid-19 infection segmentation architectures (SCOATNet and nCoVSegNet). © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

CNR-IEMN: A DEEP LEARNING BASED APPROACH TO RECOGNISE COVID-19 FROM CT-SCAN

The recognition of Covid-19 infection and distinguishing it from other Lung diseases from CT-scan is an emerging field in machine learning and computer vision community. In this paper, we proposed deep learning based approach to recognize the Covid-19 infection from the CT-scans. Our approach consists of two main stages. In the first stage, we trained deep learning architectures with Multi-task strategy for Slice-Level classification. In the second stage, we used the previous trained models with XG-boost classifier to classify the whole CT-scan into Normal, Covid-19 or Cap class. The evaluation of our approach achieved promising results on the validation data of SPGC-COVID dataset. In more details, our approach achieved 87.75% as overall accuracy and 96.36%, 52.63% and 95.83% sensitivities for Covid-19, Cap and Normal, respectively. From other hand, our approach achieved the fifth place on the three test datasets of SPGC on COVID-19 challenge where our approach achieved the best result for Covid-19 sensitivity.