Computer-Aided Diagnosis of COVID-19 from Chest X-ray Images Using Hybrid-Features and Random Forest Classifier.

In recent years, a lot of attention has been paid to using radiology imaging to automatically find COVID-19. (1) Background: There are now a number of computer-aided diagnostic schemes that help radiologists and doctors perform diagnostic COVID-19 tests quickly, accurately, and consistently. (2) Methods: Using chest X-ray images, this study proposed a cutting-edge scheme for the automatic recognition of COVID-19 and pneumonia. First, a pre-processing method based on a Gaussian filter and logarithmic operator is applied to input chest X-ray (CXR) images to improve the poor-quality images by enhancing the contrast, reducing the noise, and smoothing the image. Second, robust features are extracted from each enhanced chest X-ray image using a Convolutional Neural Network (CNNs) transformer and an optimal collection of grey-level co-occurrence matrices (GLCM) that contain features such as contrast, correlation, entropy, and energy. Finally, based on extracted features from input images, a random forest machine learning classifier is used to classify images into three classes, such as COVID-19, pneumonia, or normal. The predicted output from the model is combined with Gradient-weighted Class Activation Mapping (Grad-CAM) visualisation for diagnosis. (3) Results: Our work is evaluated using public datasets with three different train-test splits (70-30%, 80-20%, and 90-10%) and achieved an average accuracy, F1 score, recall, and precision of 97%, 96%, 96%, and 96%, respectively. A comparative study shows that our proposed method outperforms existing and similar work. The proposed approach can be utilised to screen COVID-19-infected patients effectively. (4) Conclusions: A comparative study with the existing methods is also performed. For performance evaluation, metrics such as accuracy, sensitivity, and F1-measure are calculated. The performance of the proposed method is better than that of the existing methodologies, and it can thus be used for the effective diagnosis of the disease.

Classification of Post-COVID-19 Emotions with Residual-Based Separable Convolution Networks and EEG Signals

The COVID-19 epidemic has created highly unprocessed emotions that trigger stress, anxiety, or panic attacks. These attacks exhibit physical symptoms that may easily lead to misdiagnosis. Deep-learning (DL)-based classification approaches for emotion detection based on electroencephalography (EEG) signals are computationally costly. Nowadays, limiting memory potency, considerable training, and hyperparameter optimization are always needed for DL models. As a result, they are inappropriate for real-time applications, which require large computational resources to detect anxiety and stress through EEG signals. However, a two-dimensional residual separable convolution network (RCN) architecture can considerably enhance the efficiency of parameter use and calculation time. The primary aim of this study was to detect emotions in undergraduate students who had recently experienced COVID-19 by analyzing EEG signals. A novel separable convolution model that combines residual connection (RCN-L) and light gradient boosting machine (LightGBM) techniques was developed. To evaluate the performance, this paper used different statistical metrics. The RCN-L achieved an accuracy (ACC) of 0.9263, a sensitivity (SE) of 0.9246, a specificity (SP) of 0.9282, an F1-score of 0.9264, and an area under the curve (AUC) of 0.9263 when compared to other approaches. In the proposed RCN-L system, the network avoids the tedious detection and classification process for post-COVID-19 emotions while still achieving impressive network training performance and a significant reduction in learnable parameters. This paper also concludes that the emotions of students are highly impacted by COVID-19 scenarios.

Real-Time Diagnosis System of COVID-19 Using X-Ray Images and Deep Learning.

The novel coronavirus named COVID-19 has quickly spread among humans worldwide, and the situation remains hazardous to the health system. The existence of this virus in the human body is identified through sputum or blood samples. Furthermore, computed tomography (CT) or X-ray has become a significant tool for quick diagnoses. Thus, it is essential to develop an online and real-time computer-aided diagnosis (CAD) approach to support physicians and avoid further spreading of the disease. In this research, a convolutional neural network (CNN) -based Residual neural network (ResNet50) has been employed to detect COVID-19 through chest X-ray images and achieved 98% accuracy. The proposed CAD system will receive the X-ray images from the remote hospitals/healthcare centers and perform diagnostic processes. Furthermore, the proposed CAD system uses advanced load balancer and resilience features to achieve fault tolerance with zero delays and perceives more infected cases during this pandemic.