Automatic detection of Covid-19 based on lung CT images using Deep Convolutional Neural Networks (CNN)

In recent years, the COVID-19 pandemic has spread all over the world. Due to its rapid transmission, techniques that automatically detect COVID-19 infections and distinguish it from other forms of pneumonia are crucial. The scientific community has embarked on finding solutions to quick detection of COVID-19 through implementation of deep learning(DL) techniques that can diagnose COVID-19 using computed tomography (CT) lung scans. The use of CT images has been widely accepted in medical imaging and it is a pertinent screening tool due to its higher sensitivity in recognizing early pneumonic changes. Also, most developed DL models developed have been end-to-end from feature extraction to categorization of the COVID19 infected images. The proposed model results showed high accuracy rates on both training and testing of the model in COVID-19 classification. A customised ResNet-50 architecture has the best results in classifying the images and achieved state of art accuracy of 97% on training and testing using the COVID dataset with 200 epochs. This work presented a computationally efficient and highly accurate model for multi-class classification of normal and infected individuals. The model can help in effective early screening of COVID-19 cases hence reducing the burden on healthcare systems. © 2022 IEEE.

Detection of Corona Virus from Chest X-Rays Using CNN Algorithm

COVID19's global epidemic has wreaked havoc on our lives in every aspect. Healthcare systems, to be more specific, was pushed beyond their limits. Artificial intelligence developments have paved the way for the creation of complicated applications that can meet a wide range of requirements. Precision in clinical practice is necessary. In this study, machine learning-based deep learning models that were customized and pretrained were used. Convolutional Neural Networks that's utilized from detected COVID-19 respiratory pneumonia complications. Then more number of COVID-19 patients' radiographs pictures were collected locally. In Data was also used from three publicly available datasets. There are four options for evaluating performance. The public dataset was utilized first for training and testing. Second, data from both the local and national levels]. A variety of public sources were used to train and test the models. Because all diagnostic procedures have little retrieved data at the moment, medical conciliation should examine the likelihood of incorporating X-rays into illness diagnosis based on the data, while all research-based X-ray is carried out. It is possible to approach the problem from various angle. © 2022 IEEE.

Neuro-symbolic interpretable AI for automatic COVID-19 patient-stratification based on standardised lung ultrasound data

In the current pandemic, being able to efficiently stratify patients depending on their probability to develop a severe form of COVID-19 can improve the outcome of treatments and optimize the use of the available resources. To this end, recent studies proposed to use deep-networks to perform automatic stratification of COVID-19 patients based on lung ultrasound (LUS) data. In this work, we present a novel neuro-symbolic approach able to provide video-level predictions by aggregating results from frame-level analysis made by deep-networks. Specifically, a decision tree was trained, which provides direct access to the decision process and a high-level explainability. This approach was tested on 1808 LUS videos acquired from 100 patients diagnosed as COVID-19 positive by a RT-PCR swab test. Each video was scored by LUS experts according to a 4-level scoring system specifically developed for COVID-19. This information was utilised for both the training and testing of the algorithms. A five-folds cross-validation process was utilised to assess the performance of the presented approach and compare it with results achieved by deep-learning models alone. Results show that this novel approach achieves better performance (82% of mean prognostic agreement) than a threshold-based ensemble of deep-learning models (78% of mean prognostic agreement). © 2022 Acoustical Society of America.

Evaluation of YOLO-X and MobileNetV2 as Face Mask Detection Algorithms

Even though COVID-19 still exists, people are more reluctant to wear masks in public places, in fact only 73% of Indonesian still do. Hence, automatic mask surveillance in public places is still needed. In this paper, we compare two algorithms named YOLO-X and MobileNetV2 to detect face masks. YOLO-X was able to outperform other YOLO algorithms in object detection. While, according to researchers, MobileNetV2 achieved 9S% in face mask detection. To fairly evaluate both algorithms we need to conduct research under controlled variables including using the same datasets and devices. We used public datasets which consists of 1493 mask images and 6451 non mask images for training and testing. The results show that YOLO-X outperforms MobileNetV2 as it achieves 95.0%, 98.7%, 93.7%, and 96.1% for accuracy, average precision, recall, and F1-score respectively. YOLO-X also performs better in detecting faces with occlusion such as glasses, hands, and postures than MobileNetV2. However, YOLO-X detects faces and face masks 31.9% slower than MobileNetV2. © 2022 IEEE.

Automatic Detection of Dental Lesions Based on Deep Learning

Objective Since the outbreak of COVID-19, many hospitals have become overloaded with patients seeking examination, resulting in an imbalance between medical staff and patients. These high concentrations of people in hospital settings not only aggravate the risk of cross-infection among patients, but also stall the public medical system. Consequently, mild and chronic conditions cannot be treated effectively, and eventually develop into serious diseases. Therefore, the use of deep learning to accurately and efficiently analyze X-ray images for diagnostic purposes is crucial in alleviating the pressure on medical institutions during epidemics. The method developed in this study accurately detects dental X-ray lesions, thus enabling patients to self-diagnose dental conditions. Methods The method proposed in this study employs the YOLOV5 algorithm to detect lesion areas on digital X-ray images and optimize the network model's parameters. When hospitals and medical professionals collect and label training data, they use image normalization to enhance the images. Consequently, in combination with the network environment, parameters were adjusted into four modules in the YOLOV5 algorithm. In the Input module, Mosaic data enhancement and adaptive anchor box algorithms are used to generate the initial box. The focus component was added to the Backbone module, and a CSP structure was implemented to determine the image features. When the obtained image features are input into the Backbone module, the FPN and PAN structures are used to realize feature fusion. Subsequently, GIOU_Loss function is applied to the Head moudule, and NMS non-maximum suppression is used to generate a regression of results. Results and Discussions The proposed YOLOV5-based neural network yields satisfactory training and testing results. The training algorithm produced a recall rate of 95%, accuracy rate of 95%, and F1 score of 96%. All evaluation criteria are higher than those of the target detection algorithms of SSD and Faster-RCNN (Table 1). The network converges to smoothness after loss is reduced in the training process (Fig. 6), which proves that the network successfully learns the necessary features. Thus, the difference between predicted and real values is very small, which indicates good model performance. The mAP value of network training is 0.985 (Fig. 7), which proves that the network training meets the research requirements. Finally, an observation of the visualized thermodynamic diagram reveals that the network's region of interest matches the target detection region (Fig. 8). Conclusions This study proposes the use of the YOLOV5 algorithm for detecting lesions in dental X-ray images, training and testing on the dataset, modifying the network's nominal batch size, selecting an appropriate optimizer, adjusting the weight parameters, and modifying the learning rate attenuation strategy. The model's training results were compared with those of algorithms used in previous studies. Finally, the effect of feature extraction was analyzed after the thermodynamic diagram was visualized. The experimental results show that the algorithm model detects lesion areas with an accuracy rate of more than 95%, making it an effective autonomous diagnostic tool for patients. © 2022 Science Press. All rights reserved.

A Comparative Study on COVID-19 Fake News Detection Using Different Transformer Based Models

The rapid advancement of social networks and the convenience of internet availability have accelerated the rampant spread of false news and rumors on social media sites. Amid the COVID-19 epidemic, this misleading information has aggravated the situation by putting people's mental and physical lives in danger. To limit the spread of such inaccuracies, identifying the fake news from online platforms could be the first and foremost step. In this research, the authors have conducted a comparative analysis by implementing five transformer-based models such as BERT, BERT without LSTM, ALBERT, RoBERTa, and a Hybrid of BERT & ALBERT in order to detect the fraudulent news of COVID-19 from the internet. COVID-19 Fake News Dataset has been used for training and testing the models. Among all these models, the RoBERTa model has performed better than other models by obtaining an F1 score of 0.98 in both real and fake classes. © 2022 IEEE.

FPGA-Based Deep Learning Models for Analysing Corona Using Chest X-Ray Images

Coronavirus is a large family of viruses that affects humans and damages respiratory functions ranging from cold to more serious diseases such as ARDS and SARS. But the most recently discovered virus causes COVID-19. Isolation at home or hospital depends on one's health history and conditions. The prevailing disease that might get instigated due to the existence of the virus might lead to deterioration in health. Therefore, there is a need for early detection of the virus. Recently, many works are found to be observed with the deployment of techniques for the detection based on chest X-rays. In this work, a solution has been proposed that consists of a sample prototype of an AI-based Flask-driven web application framework that predicts the six different diseases including ARDS, bacteria, COVID-19, SARS, Streptococcus, and virus. Here, each category of X-ray images was placed under scrutiny and conducted training and testing using deep learning algorithms such as CNN, ResNet (with and without dropout), VGG16, and AlexNet to detect the status of X-rays. Recent FPGA design tools are compatible with software models in deep learning methods. FPGAs are suitable for deep learning algorithms to make the design as flexible, innovative, and hardware acceleration perspective. High-performance FPGA hardware is advantageous over GPUs. Looking forward, the device can efficiently integrate with the deep learning modules. FPGAs act as a challenging substitute podium where it bridges the gap between the architectures and power-related designs. FPGA is a better option for the implementation of algorithms. The design attains 121μW power and 89 ms delay. This was implemented in the FPGA environment and observed that it attains a reduced number of gate counts and low power. © 2022 Anupama Namburu et al.

LSTM-based Model for Forecasting of COVID-19 Vaccines in Pakistan

COVID-9 has infected nearly every country on the planet. As a result, vaccinations that can reduce our risk of contracting and spreading the COVID19 virus have been developed. As a result, each government must determine how long it will take to properly vaccinate all of its population. In this study, we built an LSTM-based prediction model to anticipate vaccination coverage in Pakistan and India. The dataset contains records of vaccine updated till January 2022. To measure the losses, we have used mean absolute error (MAE), mean absolute percentage error (MAPE), mean squared error (MSE) and Root mean squared error (RMSE). The model performs very well on training and testing datasets. This model can help government in the vaccination campaign. © 2022 IEEE.

COVID-HateBERT: A Pre-trained Language Model for COVID-19 related Hate Speech Detection

With the dramatic growth of hate speech on social media during the COVID-19 pandemic, there is an urgent need to detect various hate speech effectively. Existing methods only achieve high performance when the training and testing data come from the same data distribution. The models trained on the traditional hateful dataset cannot fit well on COVID-19 related dataset. Meanwhile, manually annotating the hate speech dataset for supervised learning is time-consuming. Here, we propose COVID-HateBERT, a pre-trained language model to detect hate speech on English Tweets to address this problem. We collect 200M English tweets based on COVID-19 related hateful keywords and hashtags. Then, we use a classifier to extract the 1.27M potential hateful tweets to re-train BERT-base. We evaluate our COVID-HateBERT on four benchmark datasets. The COVID-HateBERT achieves a 14.8%-23.8% higher macro average F1 score on traditional hate speech detection comparing to baseline methods and a 2.6%-6.73% higher macro average F1 score on COVID-19 related hate speech detection comparing to classifiers using BERT and BERTweet, which shows that COVID-HateBERT can generalize well on different datasets. © 2021 IEEE.