Research on Chest X-Ray Image Diagnosis of Covid-19 Based on Improved ResNet

With the outbreak of covid-19 in 2020, timely and effective diagnosis and treatment of each covid-19 patient is particularly important. This paper combines the advantages of deep learning in image recognition, takes RESNET as the basic network framework, and carries out the experiment of improving the residual structure on this basis. It is tested on the open source new coronal chest radiograph data set, and the accuracy rate is 82.3%. Through a series of experiments, the training model has the advantages of good generalization, high accuracy and fast convergence. This paper proves the feasibility of the improved residual neural network in the diagnosis of covid-19. © 2023 SPIE.

Learning Strategies for Sensitive Content Detection

Currently, the volume of sensitive content on the Internet, such as pornography and child pornography, and the amount of time that people spend online (especially children) have led to an increase in the distribution of such content (e.g., images of children being sexually abused, real-time videos of such abuse, grooming activities, etc.). It is therefore essential to have effective IT tools that automate the detection and blocking of this type of material, as manual filtering of huge volumes of data is practically impossible. The goal of this study is to carry out a comprehensive review of different learning strategies for the detection of sensitive content available in the literature, from the most conventional techniques to the most cutting-edge deep learning algorithms, highlighting the strengths and weaknesses of each, as well as the datasets used. The performance and scalability of the different strategies proposed in this work depend on the heterogeneity of the dataset, the feature extraction techniques (hashes, visual, audio, etc.) and the learning algorithms. Finally, new lines of research in sensitive-content detection are presented.

Survey on Healing Herbs Detection using Machine Learning

Nowadays health is very important. All need to take care of their health so that they can prevent diseases and improve their quality of life. The Sanskrit word Ayurveda comprises Science and Knowledge. In simple words, we can say that Ayurveda is the art of living. Medicines can cause hazards to our bodies as well but Ayurveda uses all the natural things for treatment so it is not harmful or dangerous for our bodies. The precise identification of medicinal plants is critical in Ayurvedic medicine. Human specialists use visual characteristics and fragrances to identify plants. Along with leaves flowers and spices are also a vital component in curing diseases. Flowers like lavender, marigold, hibiscus and many more, spices like clove, ginger, cumin, turmeric and so on play crucial role along with their leaves. Covid -19 had very terrible impact on lives of many people. Along with medicines;Ayurveda also played a very important role in curing people. Ayurvedic kadas and many more vanaspatis were used to get rid of this virus, many of the people got rid of this virus at home using home remedies. So, our main aim is to predict the ayurvedic plants that can cure various diseases using machine learning models. © 2023 IEEE.

Facilitating COVID recognition from X-rays with computer vision models and transfer learning.

Multimedia data plays an important role in medicine and healthcare since EHR (Electronic Health Records) entail complex images and videos for analyzing patient data. In this article, we hypothesize that transfer learning with computer vision can be adequately harnessed on such data, more specifically chest X-rays, to learn from a few images for assisting accurate, efficient recognition of COVID. While researchers have analyzed medical data (including COVID data) using computer vision models, the main contributions of our study entail the following. Firstly, we conduct transfer learning using a few images from publicly available big data on chest X-rays, suitably adapting computer vision models with data augmentation. Secondly, we aim to find the best fit models to solve this problem, adjusting the number of samples for training and validation to obtain the minimum number of samples with maximum accuracy. Thirdly, our results indicate that combining chest radiography with transfer learning has the potential to improve the accuracy and timeliness of radiological interpretations of COVID in a cost-effective manner. Finally, we outline applications of this work during COVID and its recovery phases with future issues for research and development. This research exemplifies the use of multimedia technology and machine learning in healthcare.

Comparative approach to different convolutional neural network (CNN) architectures applied to human behavior detection.

Medical diagnostics, product classification, surveillance and detection of inappropriate behavior are becoming increasingly sophisticated due to the development of methods based on image analysis using neural networks. Considering this, in this work, we evaluate state-of-the-art convolutional neural network architectures proposed in recent years to classify the driving behavior and distractions of drivers. Our main goal is to measure the performance of such architectures using only free resources (i.e., free graphic processing unit, open source) and to evaluate how much of this technological evolution is available to regular users.

A New Respiratory Diseases Detection Model in Chest X-Ray Images Using CNN

Convolutional Neural Network (CNN)-based deep learning techniques have recently demonstrated increased potential and effectiveness in image recognition applications, such as those involving medical images. Deep-learning models can recognize targets with performance comparable to radiologists when used with CXR. The primary goal of this research is to examine a deep learning technique used on the radiography dataset to detect COVID-19 in X-ray medical images. The proposed system consists of several stages, from pre-processing, passing through the feature reduction using more than one technique, to the classification stage based on a proposed model. The test was applied to the COVID-19 Radiography dataset of normal and three lung infections (COVID-19, Viral Pneumonia, and Lung Opacity). The proposed CNN model has shown its ability to classify COVID, normal, and other lung infections with perfect accuracy of 99.94%. Consequently, the AI-based early-stage detection algorithms will be enhanced, increasing the accuracy of the X-raybased modality for the screening of various lung diseases. © 2023 Lavoisier. All rights reserved.

Advanced, Innovative AIoT and Edge Computing for Unmanned Vehicle Systems in Factories

Post-COVID-19, there are frequent manpower shortages across industries. Many factories pursuing future technologies are actively developing smart factories and introducing automation equipment to improve factory manufacturing efficiency. However, the delay and unreliability of existing wireless communication make it difficult to meet the needs of AGV navigation. Selecting the right sensor, reliable communication, and navigation control technology remains a challenging issue for system integrators. Most of today's unmanned vehicles use expensive sensors or require new infrastructure to be deployed, impeding their widespread adoption. In this paper, we have developed a self-learning and efficient image recognition algorithm. We developed an unmanned vehicle system that can navigate without adding any specialized infrastructure, and tested it in the factory to verify its usability. The novelties of this system are that we have developed an unmanned vehicle system without any additional infrastructure, and we developed a rapid image recognition algorithm for unmanned vehicle systems to improve navigation safety. The core contribution of this system is that the system can navigate smoothly without expensive sensors and without any additional infrastructure. It can simultaneously support a large number of unmanned vehicle systems in a factory.

Development of hybrid technology using Machine Learning and Block chain Technology to prevent from COVID 19 through the proper information gathering

Blockchain and artificial intelligence (AI) have shown promise in combating the Covid epidemic. Blockchain in particular may aid in early detection to fight pandemics. The methods established for infection prevention include the use of face masks, public isolation within a 6 metre radius, regular check-ups, and two doses of vaccinations.This system has features for detecting masks, people, temperature, information tracking, in-person interactions, and a person's medical history. Diseases might be monitored and their spread contained with the advancement of technology and the rise in smartphone use. Because additional economic sectors are opening up and because Covid is still spreading widely, adhering to the guidelines is more important than ever for avoiding infection. © 2023 IEEE.

An Advanced Deep Learning Medical Image Recognition and Diagnosis of Respiratory System Viruses

Respiratory infections are a confusing and time-consuming task that caused recently a pandemic that affected the whole world. One of the pandemics was COVID-19 that has exposed the vulnerability of medical services across the world, particularly in underdeveloped nations. There comes a strong demand for developing new computer-assisted diagnosis tools to present cost-effective and rapid screening in locations wherein enormous traditional testing is impossible. Medical imaging becomes critical for diagnosing disease, X-rays and computed tomography (CT) scan are employed in the deep network which will be helpful in diagnosing diseases. This paper proposes a scanning model based on using a Mel Frequency Cepstral Coefficients (MFCC) features extracted from a respiratory virus CT-Scan image and then filtered by applying Gabor filter (GF). The filtered image is passed to Convolutional Neural Network (CNN) for classifying the image for the presence of a respiratory virus such as Covid, Viral Pneumonia or being a healthy normal image. The proposed system achieved a validation accuracy of 100% with an overall accuracy of 99.44%. © 2022 IEEE.

A grid management system for COVID-19 antigen detection based on image recognition

Objective: To develop a SARS-CoV-2 antigen detection management system for Chinese residents under community grid management, which is supported by "health information technology" and "neural network image recognition", so as to give full play to the advantages of "grid management". This system is applied to the normalized prevention and control of COVID-19 epidemic. Method(s): The model of image recognition algorithm was built based on deep learning and convolution neural network (CNN) artificial intelligence algorithm. The improved Canny edge detection algorithm was used to monitor and locate the image edge, and then the image segmentation and judgment value calculation were completed according to projection method. The system construction was completed combing with the grid number design. Result(s): The proposed method had been tested and showed the accuracy of the algorithm. With a certain robustness, the algorithm error was proved to be small. Based on the image recognition algorithm model, the development of SARS-CoV-2 antigen detection management system covering user login, paper-strip test image upload, paper-strip test management, grid management, grid warning and regional traffic management was completed. Conclusion(s): Antigen detection is an important supplementary means of COVID-19 epidemic prevention and control in the new stage. The SARS-CoV-2 antigen detection management system for Chinese residents under community grid managemen based on image recognition enables mobile communication devices to recognize the image of SARS-CoV-2 antigen detection results, which is helpful to form a grid management mode for the epidemic and improve the management framework of epidemic monitoring, detection, early warning and prevention and control.Copyright © 2023 The Authors

Recognition System of Multi-model Chest X-Ray Diseases Based on YOLO v4

We use three models to build and design a multi-model identification system, and the identification results between the models are verified to increase accuracy. The identified lung diseases are classified into four categories, namely Normal, COVID-19, Tuberculosis, and Viral Pneumonia cases. After the user uploads the chest X-ray image, the system displays the results of the three identification types, and the calculation time is about 5 to 10 s. The accuracy of the multi-model system is better than that of the single-model system. If the Normal cases are included, the specificity is 77.41% for the traditional single-model system and 89.81% for the multi-model system. Additionally, if Normal cases are excluded, the F1 score is 70.00% for the single-model system and 80.7% for the multi-model system. Compared with the neural network with Faster R-CNN F1-Score of 90%, Mask R-CNN F1-Score of 85% and resNet-50 F1-Score of 80% are obtained. © 2022 IEEE.

Image Recognition Based on Deep Learning with Thermal Camera Sensing

As the COVID-19 epidemic spread across the globe, people around the world were advised or mandated to wear masks in public places to prevent its spreading further. In some cases, not wearing a mask could result in a fine. To monitor mask wearing, and to prevent the spread of future epidemics, this study proposes an image recognition system consisting of a camera, an infrared thermal array sensor, and a convolutional neural network trained in mask recognition. The infrared sensor monitors body temperature and displays the results in real-time on a liquid crystal display screen. The proposed system reduces the inefficiency of traditional object detection by providing training data according to the specific needs of the user and by applying You Only Look Once Version 4 (YOLOv4) object detection technology, which experiments show has more efficient training parameters and a higher level of accuracy in object recognition. All datasets are uploaded to the cloud for storage using Google Colaboratory, saving human resources and achieving a high level of efficiency at a low cost. © 2023 CRL Publishing. All rights reserved.

COVID-19 AI Inspector

In this paper, we aimed to aid the control measure that is implemented during the COVID-19 in Taiwan. As the virus spreads rapidly throughout the world, the Taiwanese government imposed three restrictions that help Taiwan to control the spread immediately. One of the restrictions that they imposed is to always wear a face mask. To avoid economic breakdown and still consider the general health of the public, Taiwan limits mass gatherings like in the food industry, entertainment, public transport, religious activities, etc. To be able to increase health security during a mass gathering, we developed an AI software to be able to detect people who are properly wearing a face mask, improperly wearing, and not wearing at all. The data that we used is from Kaggle to be able to use and process the data during image recognition, we use a raspberry pi board and camera. With the algorithm we used;we came up with an outstanding system where we could present excellent results due to the detection accuracy.

Researches Advanced in Medical Detection based on Deep Learning

Medical image analysis based on computer vision technology has always been a research hotspot in the community, which aims to assist doctors in diagnosis by accurately analyzing pathological images to divide the patient's condition and the patient's lesions. Thanks to the rapid development of deep learning, the application of computer image recognition technology in medicine is becoming more and more widespread, while still facing a series of challenges such as low data set data, insufficient performance of algorithms and fine delineation of lesions. In order to solve these problems, based on extensive literature research, this paper first compares the algorithms in the application for Corona Virus Disease 2019, skin cancer and liver cancer. Then we introduce the improvement of these algorithms by expanding the number of data sets, optimizing the algorithms, and fitting the neural networks and models, whcih can improve the accuracy of image recognition technology to assist doctors in identifying lesions in clinical practice. The algorithms are further compared quantitatively on the basis of the training set in clinical diseases, and the difficulties to be overcome in image recognition and the future development trend are explained and predicted from the analysis of the comparison. Many new algorithms and excellent models are being gradually improved with the development of the times, and image recognition technology will also develop towards more research fields in the future. © 2023 SPIE.

Gesture Control System: Using CNN based Hand Gesture Recognition for Touch-less Operation of Kiosk Machine

Kiosk machines have gained good popularity among the general public as they are easy to operate and provide a good interactive interface. As a result, multiple users use the kiosk machine throughout the day to find the information they are looking for. Users interact with the kiosk machine by the means of touching its screen or using the buttons. Due to this, it is observed that throughout the day hundreds or even thousands of people end up touching the surface of the kiosk machine. Because of this hygiene cannot be maintained as it is not possible to sanitize the kiosk machine after each use. This has become a serious issue considering the effects that the Covid-19 pandemic had on the world. Multiple people touching the same surface is one of the most common ways through which the virus can spread. To help deal with this problem we have designed a gesture control system using deep learning techniques through which kiosk machines can be operated in a touch-less way. © 2022 IEEE.

Detecting Tuberculosis from Vietnamese X-Ray Imaging Using Transfer Learning Approach

Deep learning created a sharp rise in the development of autonomous image recognition systems, especially in the case of the medical field. Among lung problems, tuberculosis, caused by a bacterium called Mycobacterium tuberculosis, is a dangerous disease because of its infection and damage. When an infected person coughs or sneezes, tiny droplets can bring pathogens to others through inhaling. Tuberculosis mainly damages the lungs, but it also affects any part of the body. Moreover, during the period of the COVID-19 (coronavirus disease 2019) pandemic, the access to tuberculosis diagnosis and treatment has become more difficult, so early and simple detection of tuberculosis has been more and more important. In our study,we focused on tuberculosis diagnosis by using the chestX-ray image, the essential input for the radiologist's profession, and researched the effectiveness of the transfer learning approach in the case study of Vietnamese chest X-ray images. We proposed four strategies to clarify our hypothesis in different ways of applying transfer learning and different training set types. We also prepared a Vietnamese X-ray image dataset with the support of the VRPACS team to provide the basis for training and testing deep learning models. Our experiments were carried out by applying three different architectures, Alexnet, Resnet, and Densenet, on international, Vietnamese, and combined X-ray image datasets. After training, all models were verified on a pure Vietnamese X-rays set. The results show that transfer learning is suitable in the case study of Vietnamese chest X-ray images with high evaluating metrics in terms of AUC (Area under the Receiver Operating Characteristic Curve), sensitivity, specificity, and accuracy. In the best strategy, most of the scores were more than 0.93, and all AUCs were more than 0.98. © 2023 Tech Science Press. All rights reserved.

Fusion of Bilateral 2DPCA Information for Image Reconstruction and Recognition

Being an efficient image reconstruction and recognition algorithm, two-dimensional PCA (2DPCA) has an obvious disadvantage in that it treats the rows and columns of images unequally. To exploit the other lateral information of images, alternative 2DPCA (A2DPCA) and a series of bilateral 2DPCA algorithms have been proposed. This paper proposes a new algorithm named direct bilateral 2DPCA (DB2DPCA) by fusing bilateral information from images directly-that is, we concatenate the projection results of 2DPCA and A2DPCA together as the projection result of DB2DPCA and we average between the reconstruction results of 2DPCA and A2DPCA as the reconstruction result of DB2DPCA. The relationships between DB2DPCA and related algorithms are discussed under some extreme conditions when images are reshaped. To test the proposed algorithm, we conduct experiments of image reconstruction and recognition on two face databases, a handwritten character database and a palmprint database. The performances of different algorithms are evaluated by reconstruction errors and classification accuracies. Experimental results show that DB2DPCA generally outperforms competing algorithms both in image reconstruction and recognition. Additional experiments on reordered and reshaped databases further demonstrate the superiority of the proposed algorithm. In conclusion, DB2DPCA is a rather simple but highly effective algorithm for image reconstruction and recognition.

CoV-TI-Net: Transferred Initialization with Modified End Layer for COVID-19 Diagnosis

This paper proposes transferred initialization with modified fully connected layers for COVID-19 diagnosis. Convolutional neural networks (CNN) achieved a remarkable result in image classification. However, training a high-performing model is a very complicated and time-consuming process because of the complexity of image recognition applications. On the other hand, transfer learning is a relatively new learning method that has been employed in many sectors to achieve good performance with fewer computations. In this research, the PyTorch pre-trained models (VGG19_bn and WideResNet -101) are applied in the MNIST dataset for the first time as initialization and with modified fully connected layers. The employed PyTorch pre-trained models were previously trained in ImageNet. The proposed model is developed and verified in the Kaggle notebook, and it reached the outstanding accuracy of 99.77% without taking a huge computational time during the training process of the network. We also applied the same methodology to the SIIM-FISABIO-RSNA COVID-19 Detection dataset and achieved 80.01% accuracy. In contrast, the previous methods need a huge compactional time during the training process to reach a high-performing model. Codes are available at the following link: github.com/dipuk0506/Spina1Net © 2022 IEEE.

Detection of COVID-19 Using Denoising Autoencoders and Gabor Filters

As of 2019, COVID-19 is the most difficult issue that we are facing. Till now, it has reached over 30 million deaths. Since SARS-CoV-2 is the new virus, it took time to investigate and examine the influence of Coronavirus in human. After analyzing the spreading and infection of COVID-19, researchers applied Artificial Intelligence (AI) techniques to detect COVID-19 quickly to balance the rapid spreading of the virus. Image segmentation is a critical first step in clinical implementations, is a vital role in computer - aided diagnosis that relies heavily on image recognition. Image segmentation is used in medical MRI research to determine the proportions of different anatomical areas of the tissue, as well as how they change as the disease progresses. CT scans are often used to aid with diagnoses. Computer-assisted therapy (CAD) using AI is a particularly significant research area in intelligent healthcare. This paper presents the detection of COVID-19 at an early stage using autoencoders algorithm and Generative Adversarial Networks (GAN) using deep learning approach with more accurate results. The images of Chest Radiograph (CRG) and Chest Computed Tomography (CCT) are used as a trained dataset to detect since SARS-CoV-2 first affect the respiratory system in humans. We achieved a ratio of 1.0, 0.99, and 0.96, the combined dataset was randomly divided into the train, validation, and test sets. Although the early detection of Coronavirus is still a question since the accuracy of the deep learning approach is still under research. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Convenient Nucleic Acid Detection Method and Point-of-Care Detection Device Rased on CRISPR/Cas12a Molecular Diagnosis

Objective Clustered regularly interspaced short palindromic repeats (CRISPR) has shown significant promise as an emerging nucleic acid detection technology. However, it still requires improvement in terms of sensitivity, detection automation, and anti-pollution. Furthermore, CRISPR technology lacks simple and portable professional equipment to meet the high demand of rapid point-of-care testing. Therefore, this study proposes a CRISPR/Cas12a detection reaction system for SARS-CoV-2. This detection response system and innovative tube-in-tube consumables aid in developing a portable compact device for simultaneous automatic detection of several samples and a coaxial fiber-based fluorescence detection system. Finally, we developed a single-sample user-friendly nucleic acid detection APP based on smartphone recognition and detection results for the manual detection mode. Methods The target in this study was severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which was detected using the CRISPR method and enhanced via the reverse transcription-recombinase polymerase amplification (RT-RPA) technique;the feasibility was assessed using the reverse transcription-polymerase chain reaction (RT-PCR) amplification method in the early stages. Various companies customized the required reagents and the designed sequences. In the detection process, first, with the tube-in-tube consumables developed by our team in the early stage, which comprised the reaction outer and inner tubes, the amplification reagents and detection reagents were loaded into the inner and outer tubes, respectively. The temperature was regulated to 37-42 ℃ to complete the amplification. The reagents in the inner and outer tubes were then mixed by shaking or centrifugation, and the temperature was adjusted to complete the CRISPR reaction. Finally, it was possible to observe if there was any fluorescence occurrence under the illumination of a blue light. The detection instrument was composed of an optical cassette and a base, and automatic detection was realized through a printed circuit board (PCB), a human-computer interaction display screen, etc. In addition, this study also used the fluorescence image recognition algorithm to process the detection images, compared with the international standard polymerase chain reaction (PCR) technology to explore the detection limit, and increased the target types to test the specificity strength. Results and Discussions The lower part of the detection instrument designed by our team integrates the printed circuit board and the human-computer interaction display screen. In the automatic detection mode, the fluorescence recognition circuit was designed with the help of a 470 nm light-emitting diode (LED), an optical filter, a complementary metal oxide semiconductor (CMOS) camera, a collimating lens, and a coaxial fiber. At the same time, the specificity of the theoretical experiment was verified through comparative experiments on several different targets. In addition, to verify the accuracy of this method for detecting actual samples, we compared each actual sample through PCR detection and the method based on the combination of RT-RPA and CRISPR proposed in this study. The detection results showed that the two were perfectly consistent. Conclusions The current study proposed a CRISPR/Cas12a-based anti-pollution portable nucleic acid detection technique. Furthermore, a simple model was proposed based on the naked eye or smartphone to recognize results;additionally, a downsized portable device based on fluorescence detection that can simultaneously detect numerous samples was constructed. The portable device can detect numerous samples simultaneously, and it has a constant heating mechanism and fluorescence stimulation detection optical channel to enhance the detection system’s accuracy and stability. The nucleic acid of SARS-CoV-2 was verified using the proposed method and detection system. The minimum detection limit was <10 copy/μL. The test findings of our method had a good consistency with that of real- ime fluorescence quantitative PCR method, but our method took less than half the time consuming of the PCR method, and the whole detection process could be finished in 32 min. The method and technology developed in this study propose a novel approach for nucleic acid detection at health-care center and home. © 2022 Science Press. All rights reserved.