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.

Deep efficient-nets with transfer learning assisted detection of COVID-19 using chest X-ray radiology imaging.

Corona Virus (COVID-19) could be considered as one of the most devastating pandemics of the twenty-first century. The effective and the rapid screening of infected patients could reduce the mortality and even the contagion rate. Chest X-ray radiology could be designed as one of the effective screening techniques for COVID-19 exploration. In this paper, we propose an advanced approach based on deep learning architecture to automatic and effective screening techniques dedicated to the COVID-19 exploration through chest X-ray (CXR) imaging. Despite the success of state-of-the-art deep learning-based models for COVID-19 detection, they might suffer from several problems such as the huge memory and the computational requirement, the overfitting effect, and the high variance. To alleviate these issues, we investigate the Transfer Learning to the Efficient-Nets models. Next, we fine-tuned the whole network to select the optimal hyperparameters. Furthermore, in the preprocessing step, we consider an intensity-normalization method succeeded by some data augmentation techniques to solve the imbalanced dataset classes' issues. The proposed approach has presented a good performance in detecting patients attained by COVID-19 achieving an accuracy rate of 99.0% and 98% respectively using training and testing datasets. A comparative study over a publicly available dataset with the recently published deep-learning-based architectures could attest the proposed approach's performance.

An Intelligent Sensor Based Decision Support System for Diagnosing Pulmonary Ailment through Standardized Chest X-ray Scans.

Academics and the health community are paying much attention to developing smart remote patient monitoring, sensors, and healthcare technology. For the analysis of medical scans, various studies integrate sophisticated deep learning strategies. A smart monitoring system is needed as a proactive diagnostic solution that may be employed in an epidemiological scenario such as COVID-19. Consequently, this work offers an intelligent medicare system that is an IoT-empowered, deep learning-based decision support system (DSS) for the automated detection and categorization of infectious diseases (COVID-19 and pneumothorax). The proposed DSS system was evaluated using three independent standard-based chest X-ray scans. The suggested DSS predictor has been used to identify and classify areas on whole X-ray scans with abnormalities thought to be attributable to COVID-19, reaching an identification and classification accuracy rate of 89.58% for normal images and 89.13% for COVID-19 and pneumothorax. With the suggested DSS system, a judgment depending on individual chest X-ray scans may be made in approximately 0.01 s. As a result, the DSS system described in this study can forecast at a pace of 95 frames per second (FPS) for both models, which is near to real-time.