Automated Detection of Covid-19Waves with Computerized Tomography Scan Using Deep Learning

Covid-19 is a newly found corona virus that causes an infectious disease. An accurate diagnosis of several waves in Covid-19 is still a tremendous confront due to the difficulties of marking infection areas, and it is an emergency and important for worldwide in 2020 and still now. There is almost no difference between common pneumonia and other viral pneumonia using CT scanned images, so false-negative images may be obtained. An ensemble of deep multi-instance learning (DMIL), train a blotch-level classifier and view the chest CT images as a bag of samples to avoid false negative. Mask R-CNN is used to train an image-level classifier that labels input image as common pneumonia or Covid pneumonia. These Ensemble models of DMIL with Mask R-CNN show an accuracy of 98.96%. These advantages make our model an efficient tool in the screening of Covid-19. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Face Mask detection using Mask R-CNN to control the spread of Covid-19

Business executives are developing cutting-edge digital solutions as the virus outbreak spreads. A face mask detection system is one of them, and it can be used to spot people wearing them. Face mask identification software and applications have already been released by a few businesses, and others have promised to do the same for the service. The proposed work examines face mask detection accuracy using CNN networks. Mask wear is now required in many developed and developing countries worldwide when leaving the house or entering public spaces. It will be difficult to maintain touchless access control in buildings while recognising faces wearing masks on any surveillance systems. Masks covering faces has made face detection algorithms and performance difficult. The proposed work detect face mask labeled no mask or mask with detection accuracy. The work train the system to click images of a face and provide labeled data. The work is classified using Convolution Neural Network (CNN), a Deep learning technique, to classify the input image with the help of the classification algorithm MobileNetV2. The trained system shows whether a person in the video frame is wearing a mask or not. © 2023 IEEE.

Real-Time Face Mask Detection using Computer Vision and Machine Learning

Face mask detection is the process of identifying whether a person is wearing a face mask or not in real-time through the use of computer vision and machine learning algorithms. This technology can be used in various applications, such as security systems at public transportation hubs or in hospitals, to ensure compliance with health and safety regulations during a pandemic or other infectious disease outbreaks. The technology works by analyzing images or video streams from cameras and computer vision techniques are used to detect the presence of a face mask on a person's face. The output of the system is a binary result (i.e., mask detected or not detected) or a more detailed result that provides information about the type of mask and its location on the face. © 2023 IEEE.

PTZ-Camera-Based Facial Expression Analysis using Faster R-CNN for Student Engagement Recognition

During the pandemic, online classes are predominated. However, the new normal needs effective analysis of students' classroom engagement. Offline classes also have a potential threat to students' engagement before and especially after the post-covid. Facial Expressions Analysis has become essential in the learning environment, whether it is online or offline. The offline classroom environment is considered a problem environment. Since, it can be easily adapted to the online environment. Notably, in the PTZ camera environment, the recognition becomes more challenging due to varying face poses, limited Field-of-View (FOV), illumination conditions, effects of the continuous pan, zoom-in, and zoom-out. In this paper, facial expression-based student engagement analysis in a classroom environment is proposed. Face detection has been achieved by YOLO (You only look once) detector to find multiple faces in the classroom with maximum speed and accuracy. Consequently, by adopting the Ensemble of Robust Constrained Local Models (ERCLM) method, landmark points are localized in detected faces even in occlusion, and therefore, feature matching is performed. Besides, the matched landmark points are aligned by an affine transformation. Finally, having different expressions, the aligned faces are fed as input to Faster R-CNN (Faster Regions with Convolutional Neural Network). It recognizes behavioral activities such as Attentiveness (Zero-In (ZI)), Non-Attentiveness (NA), Day Dreaming (DD), Napping (N), Playing with Personal Stuff in Private (PPSP), and Talking to the Students' Behind (TSB). The proposed approach is demonstrated using the TCE classroom datasets and Online datasets. The proposed framework outperforms the state-of-the-art algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Conclusion and future direction in data mining and machine learning

Data becomes a new currency for the world. Due to COVID-19, a significantly fewer number of flights are running, and hence the scientists cannot forecast the weather accurately. The data capturing also goes low because of this smaller number of flights. Data mining techniques play a vital role in collecting data for prediction and forecasting using different machine learning techniques. Recommender systems are available at all emerging places like agriculture, admission, matchmaking, traveling, share market, housing loan, parenting, nutrition, and consultation. Cybersecurity and forensics are also very challenging domains to fight with cybercrimes. Only data can save an entity from cyber-attacks. This chapter concludes with the future direction in data mining and machine learning techniques dealing with some related issues. © 2022 Scrivener Publishing LLC. All rights reserved.

Population Index and Analysis Based on Different Geographies, Using Distance Measurement, Social Distancing, and Deep Learning

Nowadays, every individual is familiar with the COVID-19 pandemic which has caused great turmoil in everyone's life. Also, they are aware that there is no medicine or drug to cure COVID immediately, and people are at the risk of losing their lives. Lack of vaccines or delay in vaccine production for mass results social distancing being the only measure to tackle this pandemic. As a result, social distancing has proven to be a very reliable and efficient way to diminish the growth of this disease;the reason why lockdowns are imposed, and people are asked to keep some distance from each other, for their safety as there will be minimal physical contact. Machine learning and artificial intelligence come into the picture in every solution to a generic problem the community faces nowadays like in medical, supply chain management, face detection, etc. Using the power of AI algorithms, the paper aims to develop a robust system to monitor and analyze social distance measurement protocols at public places during the COVID-19 pandemic with the help of CCTV feed and check whether they abide by the safety protocols or not by measuring the distance between them. The proposed approach is implemented to enumerate the number of violations at a popular public place to prevent massive crowds at particular periods. The proposed method is suitable to construct a scrutiny system at a public place to alert people and eschew mass gatherings that can be concluded using achieved results. The paper also has an analysis of the performance of different models of R-CNN, Fast R-CNN, and YOLO. YOLO architectures are validated based on object detection and object tracking rate in real time. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Automatic detection and classification of land subsidence in deltaic metropolitan areas using distributed scatterer InSAR and Oriented R-CNN

Multi-temporal interferometric synthetic aperture radar (InSAR) is an effective tool for measuring large-scale land subsidence. However, the measurement points generated by InSAR are too many to be manually analyzed, and automatic subsidence detection and classification methods are still lacking. In this study, we developed an oriented R-CNN deep learning network to automatically detect and classify subsidence bowls using InSAR measurements and multi-source ancillary data. We used 541 Sentinel-1 images acquired during 2015–2021 to map land subsidence of the Guangdong-Hong Kong-Macao Greater Bay Area by resolving persistent and distributed scatterers. Multi-source data related to land subsidence, including geological and lithological, land cover, topographic, and climatic data, were incorporated into deep learning, allowing the local subsidence to be classified into seven categories. The results showed that the oriented R-CNN achieved an average precision (AP) of 0.847 for subsidence detection and a mean AP (mAP) of 0.798 for subsidence classification, which outperformed the other three state-of-the-art methods (Rotated RetinaNet, R3Det, and ReDet). An independent effect analysis showed that incorporating all datasets improved the AP by 11.2% for detection and the mAP by 73.9% for classification, respectively, compared with using InSAR measurements only. Combining InSAR measurements with globally available land cover and digital elevation model data improved the AP for subsidence detection to 0.822, suggesting that our methods can be potentially transferred to other regions, which was further validated this using a new dataset in Shanghai. These results improve the understanding of deltaic subsidence and facilitate geohazard assessment and management for sustainable environments. • Land subsidence of the GBA from 2015 to 2021 was measured by PS/DS detection. • The oriented R-CNN was applied to automatically identify local subsidence. • Incorporating multi-source data improved the performance of subsidence detection. • COVID-19 lockdown ceased groundwater extraction and decelerated subsidence. [ABSTRACT FROM AUTHOR] Copyright of Remote Sensing of Environment is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

COVID-19 Detection from Chest X-Ray Images Using Detectron2 and Faster R-CNN

The COVID-19 outbreak has been causing immense damage to global health and has put the world under tremendous pressure since early 2020. The World Health Organization (WHO) has declared in March 2020 the novel coronavirus outbreak as a global pandemic. Testing of infected patients and early recognition of positive cases is considered a critical step in the fight against COVID-19 to avoid further spreading of this epidemic. As there are no fast and accurate tools available till now for the detection of COVID-19 positive cases, the need for supporting diagnostic tools has increased. Any technological method that can provide rapid and accurate detection will be very useful to medical professionals. However, there are several methods to detect COVID-19 positive cases that are typically performed based on chest X-ray images that contain relevant information about the COVID-19 virus. This paper goal is to introduce a Detectron2 and Faster R-CNN to diagnose COVID-19 automatically from X-ray images. In addition, this study could support non-radiologists with better localization of the disease by visual bounding box. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Automatic detection of A-line in lung ultrasound images using deep learning and image processing.

BACKGROUND: Auxiliary diagnosis and monitoring of lung diseases based on lung ultrasound (LUS) images is important clinical research. A-line is one of the most common indicators of LUS that can offer support for the assessment of lung diseases. A traditional A-line detection method mainly relies on experienced clinicians, which is inefficient and cannot meet the needs of these areas with backward medical level. Therefore, how to realize the automatic detection of A-line in LUS image is important. PURPOSE: In order to solve the disadvantages of traditional A-line detection methods, realize automatic and accurate detection, and provide theoretical support for clinical application, we proposed a novel A-line detection method for LUS images with different probe types in this paper. METHODS: First, the improved Faster R-CNN model with a selection strategy of localization box was designed to accurately locate the pleural line. Then, the LUS image below the pleural line was segmented for independent analysis excluding the influence of other similar structures. Next, image-processing methods based on total variation, matched filter, and gray difference were applied to achieve the automatic A-line detection. Finally, the "depth" index was designed to verify the accuracy by judging whether the automatic measurement results belong to corresponding manual results (±5%). In experiments, 3000 convex array LUS images were used for training and validating the improved pleural line localization model by five-fold cross validation. 850 convex array LUS images and 1080 linear array LUS images were used for testing the trained pleural line localization model and the proposed image-processing-based A-line detection method. The accuracy analysis, error statistics, and Harsdorff distance were employed to evaluate the experimental results. RESULTS: After 100 epochs, the mean loss value of training and validation set of improved Faster R-CNN model reached 0.6540 and 0.7882, with the validation accuracy of 98.70%. The trained pleural line localization model was applied in the testing set of convex and linear probes and reached the accuracy of 97.88% and 97.11%, respectively, which were 3.83% and 8.70% higher than the original Faster R-CNN model. The accuracy, sensitivity, and specificity of A-line detection reached 95.41%, 0.9244%, 0.9875%, and 94.63%, 0.9230%, and 0.9766% for convex and linear probes, respectively. Compared to the experienced clinicians' results, the mean value and p value of depth error were 1.5342 ± 1.2097 and 0.9021, respectively, and the Harsdorff distance was 5.7305 ± 1.8311. In addition, the accumulated accuracy of the two-stage experiment (pleural line localization and A-line detection) was calculated as the final accuracy of the whole A-line detection system. They were 93.39% and 91.90% for convex and linear probes, respectively, which were higher than these previous methods. CONCLUSIONS: The proposed method combining image processing and deep learning can automatically and accurately detect A-line in LUS images with different probe types, which has important application value for clinical diagnosis.

Technology Development and Applications as solutions to COVID-19 pandemic

"NECESSITY IS THE MOTHER OF INVENTION'' The emergency of protecting people from COVID-19 pandemic accelerated Technology development and Applications in various field. Technology became a part and parcel in one's life. Information technology played a vital role in diagnosing, medicating, communicating, and scaling down COVID-19. COVID -19 pandemic became uphill battle for the scholars, scientists, researchers of Information technology, medicine, health care system, education, economy of every country. The immediate necessity for new applications and technology in grappling pandemic situation instituted sophisticated software, medical devices, and applications. COVID- 19 made up people mind for future pandemic. This article further discusses about how Technology Development and Applications became a solution to COVID-19 pandemic. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

Prognosticating various acute covid lung disorders from COVID-19 patient using chest CT Images.

The global spread of coronavirus illness has surged dramatically, resulting in a catastrophic pandemic situation. Despite this, accurate screening remains a significant challenge due to difficulties in categorizing infection regions and the minuscule difference between typical pneumonia and COVID (Coronavirus Disease) pneumonia. Diagnosing COVID-19 using the Mask Regional-Convolutional Neural Network (Mask R-CNN) is proposed to classify the chest computerized tomographic (CT) images into COVID-positive and COVID-negative. Covid-19 has a direct effect on the lungs, causing damage to the alveoli, which leads to various lung complications. By fusing multi-class data, the severity level of the patients can be classified using the meta-learning few-shot learning technique with the residual network with 50 layers deep (ResNet-50) as the base classifier. It has been tested with the outcome of COVID positive chest CT image data. From these various classes, it is possible to predict the onset possibilities of acute COVID lung disorders such as sepsis, acute respiratory distress syndrome (ARDS), COVID pneumonia, COVID bronchitis, etc. The first method of classification is proposed to diagnose whether the patient is affected by COVID-19 or not; it achieves a mean Average Precision (mAP) of 91.52% and G-mean of 97.69% with 98.60% of classification accuracy. The second method of classification is proposed for the detection of various acute lung disorders based on severity provide better performance in all the four stages, the average accuracy is of 95.4%, the G-mean for multiclass achieves 94.02%, and the AUC is 93.27% compared with the cutting-edge techniques. It enables healthcare professionals to correctly detect severity for potential treatments.

Deep Learning-Assisted Droplet Digital PCR for Quantitative Detection of Human Coronavirus.

Since coronavirus disease 2019 (COVID-19) pandemic rapidly spread worldwide, there is an urgent demand for accurate and suitable nucleic acid detection technology. Although the conventional threshold-based algorithms have been used for processing images of droplet digital polymerase chain reaction (ddPCR), there are still challenges from noise and irregular size of droplets. Here, we present a combined method of the mask region convolutional neural network (Mask R-CNN)-based image detection algorithm and Gaussian mixture model (GMM)-based thresholding algorithm. This novel approach significantly reduces false detection rate and achieves highly accurate prediction model in a ddPCR image processing. We demonstrated that how deep learning improved the overall performance in a ddPCR image processing. Therefore, our study could be a promising method in nucleic acid detection technology.

Anomaly Detection in Red Reflex Images Using Deep Learning Approaches

Amblyopia is a noteworthy disease in children leading to visual loss. This work focuses on creating a deep learning model for the detection of Amblyopia factors in patients wearing masks under the COVID-19 pandemic. © 2022 IEEE.

Covid-19 Detection Based on Lung Lesion Signs in Big Data Processing Environment

The world is going through a global health crisis known as the Covid-19 pandemic. Currently, the outbreak is still evolving in a complicated way with a high spreading speed and new variants appearing constantly. RT-PCR test is preferred to test a patient infected with Covid-19. However, this method depends on many factors such as the time of specimen collection and preservation procedure. The cost to perform the RT-PCR test is quite high and requires a system of specialized machinery for sample analysis. Using deep learning techniques on medial images provides promising results with high accuracy with recent technological advancements. In this study, we propose a deep learning method based on CasCade R-CNN ResNet-101 and CasCade R-CNN EfficientNet in a big data processing environment that accelerates the detection of Covid-19 infections on chest X-rays. Chest X-ray can quickly be performed in most medical facilities and provides important information in detecting suspected Covid-19 cases in an inexpensive way. Experimental results show that the classification of lung lesions infected with Covid-19 has an accuracy of 96% and mAP of 99%. This method effectively supports doctors to have more basis to identify patients infected with Covid-19 for timely treatment. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Pneumonia and COVID-19 Detection in Chest X-rays Using Faster Region-Based Convolutional Neural Networks (Faster R-CNN)*

The arising of SARS-CoV-2 or 2019 novel coron-avirus in December 2019 have prioritized research on pulmonary diseases diagnosis and prognosis, especially using artificial intelligence (AI) and Deep Learning (DL). Polymerase Chain Reaction (PCR) is the most widely used technique to detect SARS-CoV-2, with a 0.12% false negative rate. While 75% of the hospitalized cases develop pneumonia caused by the virus, patients can still develop bacterial pneumonia. COVID-19 pneumonia can be diagnosed based on clinical data and Computed Tomography (CT scan). However, Chest X-rays are faster, cheaper, emit less radiations, and can be performed on bed-side. In this article, we extend the application of VGG-16 based Faster Region-Based Convolutional Neural Network (Faster R-CNN) to the detection of Pneumonia and COVID-19 in Chest X-ray images using several public datasets of total images count ranging from 2122 to 18455 Chest X-rays, and study the impact of several hyper-parameters such as objectness threshold and epochs count and length, to optimize the model's performance. Our results comply with the state of the art of Faster R-CNN in pneumonia detection as the best accuracy achieved is 65%. For COVID-19 detection, Faster R-CNN achieves a 90% validation accuracy. © 2022 IEEE.

Research on Mask Wearing Detection Based on Faster RCNN

In the context of the global raging of the new coronavirus (COVID-19), to effectively prevent the spread of the new coronavirus in the crowd, many places require the wearing of masks in public places. In response to this problem, this paper proposes a mask wearing detection based on the FasterRCNN algorithm. The method uses ResNet-50 to extract convolution features and selects high-quality suggestion boxes through NMS (non-maximum suppression), which increases the detection of incorrectly wearing masks, which can play a reminder role in practical applications and further improve the prevention of epidemics, and the final experiments show that the wearing of masks can be accurately and efficiently detected through the steps of feature extraction and prediction frame generation. © 2022 IEEE.

COVID-19 Along with Other Chest Infection Diagnoses Using Faster R-CNN and Generative Adversarial Network

The rapid spreading of coronavirus (COVID-19) caused severe respiratory infections affecting the lungs. Automatic diagnosis helps to fight against COVID-19 in community outbreaks. Medical imaging technology can reinforce disease monitoring and detection facilities with the advancement of computer vision. Unfortunately, deep learning models are facing starvation of more generalized datasets as the data repositories of COVID-19 are not rich enough to provide significant distinct features. To address the limitation, this article describes the generation of synthetic images of COVID-19 along with other chest infections with distinct features by empirical top entropy-based patch selection approach using the generative adversarial network. After that, a diagnosis is performed through a faster region-based convolutional neural network using 6,406 synthetic as well as 3,933 original chest X-ray images of different chest infections, which also addressed the data imbalance problems and not recumbent to a particular class. The experiment confirms a satisfactory COVID-19 diagnosis accuracy of 99.16% in a multi-class scenario.

Intelligent Monitoring Model for Aggregated Infection Risk 计Against the Background of COVID-19 Epidemic

The Corona Virus Disease 2019（COVID-19）epidemic is a serious threat to people’s lives.Supervision of the density of clustered people and wearing of masks is key to controlling the virus.Public places are characterized by a dense flow of people and high mobility.Manual monitoring can easily increase the risk of infection，and existing mask detection algorithms based on deep learning suffer from the limitation of having a single function and can be applied to only a single type of scenes；as such，they cannot achieve multi-category detection across multiple scenes. Furthermore，their accuracy needs to be improved. The Cascade-Attention R-CNN target detection algorithm is proposed for realizing the automatic detection of aggregations in areas，pedestrians，and face masks. Aiming to solve the problem that the target scale changes too significantly during the task，a high-precision two-stage Cascade R-CNN target detection algorithm is selected as the basic detection framework. By designing multiple cascaded candidate classification regression networks and adding a spatial attention mechanism，we highlight the important features of the candidate region features and suppress noise features to improve the detection accuracy. Based on this，an intelligent monitoring model for aggregated infection risk is constructed，and the infection risk level is determined by combining the outputs of the proposed algorithm. The experimental results show that the model has high accuracy and robustness for multi-category target images with different scenes and perspectives. The average accuracy of the Cascade Attention R-CNN algorithm reaches 89.4%， which is 2.6 percentage points higher than that of the original Cascade R-CNN algorithm，and 10.1 and 8.4 percentage points higher than those of the classic two-stage target detection algorithm，Faster R-CNN and the single-stage target detection framework，RetinaNet，respectively. © 2022, Editorial Office of Computer Engineering. All rights reserved.

Real Time Monitoring of Social Distancing using Transformers

The current census has observed, maintaining Social Distance in public places is one of the most significant factor in curbing the spread of the Corona Virus. This makes it essential for the authorities of these public places, governmental or non-governmental, to monitor the proper execution of this protocol. The risks of virus spread can be minimized by avoiding physical contact among people. This notion of monitoring Social Distance is trending and raw in its development. Although there are available solutions to this problem using YOLO Model or Tensolflow Object detection api, the purpose of this project is to provide a deep learning model for social distance tracking. In Deep Learning and Artificial Intelligence, Transformers is a technology that has its traditional application in the Natural Language Processing, thought it's application in object detection is novel and intuitive. This has techniques that use self-attention to overcome the limitations presented by inductive convolutional biases in an efficient way. Here the individuals that are detected using this model will have a bounding box specific to that person's dimensions and physical location on the image plane. The centroid of these bounding boxes will act as coordinate point of location and relative euclidean distances between these points will be used as a parameter to differentiate between the followers and violators of the said protocol. A violation threshold is also established to evaluate whether or not the distance value infringes the minimum social distance threshold. In This project we are working with the concepts of Computer Vision and Deep Learning algorithms to handle the task. © 2022 IEEE.

Crowd and Group Detection in Shopping Centre Using Mask R-CNN

This paper presents a mask R-CNN model and a dataset to detect crowd and group of people in one of the shopping areas in Indonesia. The dataset presented is the real condition of people behavior during Covid-19 pandemic. The dataset created have a total of 760 Pictures with more than 4500 total annotated objects classified in three classes (Person, Group, and Crowd) that is divided into train, validation, and test dataset. The model then created using Mask R-CNN with Resnet 101 Backbone using COCO Pre-weight. Using a dynamic learning rate, the model has an accuracy of 88.35% and 0.772 validation-loss while tested on test dataset. Our contribution includes creating a workflow of mask R-CNN implementation that could be implemented in various shopping centers and a Crowd detection model that can be use to create an automatic monitoring system. © 2022 IEEE.