Covid 19 prediction system using CNN

Today, the whole world is fighting the war against Coronavirus. The spread of the virus has been observed in almost all the parts of the world. Covid-19 also known as SARS-Cov-2 was initially observed in China which rapidly multiplied all over the world. The disease is said to spread by cough, normal cold, sneezing or when a person is in close contact with someone who is already infected. Therefore, the spread of the virus can occur when there is direct contact with an infected person or with the objects touched by the infected person. Hence, it is important to detect the contiguous spread of the virus and control it by taking appropriate measures. Several deep learning models have been used in detecting many diseases like Malaria disease, Lung infection, Parkinson's disease etc. Likewise, CNN model along with other transfer techniques is best proven to detect whether a person is infected with covid positive or not. The dataset consists of 1000 images of covid positive and normal x-rays. The proposed model has been trained and tested on the image dataset with the help of transfer learning models in order to improve the performance of the model. The models VGG-16, ResNet-50, Inception v3 and Xception have achieved an overall accuracy of 93%,82%,96% and 92% respectively. The performance of all the 4 architectures are analyzed, understood and hence presented in this paper. It is hence important to classify and detect covid positive infection and contribute towards making the world Covid-free. © 2023 Author(s).

An empirical analysis of deep ensemble approach on COVID-19 and tuberculosis X-ray images

COVID-19 is a pandemic and a highly contagious disease that can severely damage the respiratory organs. Tuberculosis is also one of the leading respiratory diseases that affect public health. While COVID-19 has pushed the world into chaos and tuberculosis is still a persistent problem in many countries. A chest X-ray can provide plethora of information regarding the type of disease and the extent of damage to the lungs. Since X-rays are widely accessible and can be used in the diagnosis of COVID-19 or tuberculosis, this study aims to leverage those property to classify them in the category of COVID-19 infected lungs, tuberculosis infected lungs or normal lungs. In this paper, an ensemble deep learning model consisting of pre-trained models for feature extraction is used along with machine learning classifiers to classify the X-ray images. Various ensemble models were implemented and highest achieved accuracy among them was observed as 93%.

Federated learning based Covid-19 detection.

The world is affected by COVID-19, an infectious disease caused by the SARS-CoV-2 virus. Tests are necessary for everyone as the number of COVID-19 affected individual's increases. So, the authors developed a basic sequential CNN model based on deep and federated learning that focuses on user data security while simultaneously enhancing test accuracy. The proposed model helps users detect COVID-19 in a few seconds by uploading a single chest X-ray image. A deep learning-aided architecture that can handle client and server sides efficiently has been proposed in this work. The front-end part has been developed using StreamLit, and the back-end uses a Flower framework. The proposed model has achieved a global accuracy of 99.59% after being trained for three federated communication rounds. The detailed analysis of this paper provides the robustness of this work. In addition, the Internet of Medical Things (IoMT) will improve the ease of access to the aforementioned health services. IoMT tools and services are rapidly changing healthcare operations for the better. Hopefully, it will continue to do so in this difficult time of the COVID-19 pandemic and will help to push the envelope of this work to a different extent.

Transfer learning-based CNN diagnostic framework for diagnosis of COVID-19 from lung CT images

COVID-19 is an infectious disease that affects the respiratory system. To assist the physician in diagnosing lung disorders from chest CT images various systems have been developed and used. Detection of COVID-19 remains a challenging area of research. The objective of the work is to develop an inductive parameter-transfer learning-based approach for the prediction of COVID-19, pneumonia, from lung CT images. Our proposed approach is built on layer wise and convolution block-wise fine-tuning which designs the CNN architecture highly specific to lung CT image. We implemented the DenseNet201, InceptionV3, Xception, VGG19, and ResNet50 as baseline models. The network architectures are developed to learn feature representation of lung CT images. For the experimental analysis, five datasets are used. From the experimental results, it is inferred that the DenseNet201 model yields higher accuracy of 0.94 for Adam optimizer and 0.93 for the RMSprop optimizer compared to other models.

Improving ransomware detection based on portable executable header using xception convolutional neural network

All malware are harmful to computer systems;however, crypto-ransomware specifically leads to irreparable data loss and causes substantial economic prejudice. Ransomware attacks increased significantly during the COVID-19 pandemic, and because of its high profitability, this growth will likely persist. To respond to these attacks, we apply static analysis to detect ransomware by converting Portable Executable (PE) header files into color images in a sequential vector pattern and classifying these via Xception Convolutional Neural Network (CNN) model without transfer learning, which we call Xception ColSeq. This approach simplifies feature extraction, reduces processing load, and is more resilient against evasion techniques and ransomware evolution. The proposed method was evaluated using two datasets. The first contains 1000 ransomware and 1000 benign applications, on which the model achieved an accuracy of 93.73%, precision of 92.95%, recall of 94.64%, and F-measure of 93.75%. The second dataset, which we created and have made available, contains 1023 ransomware, grouped in 25 still active and relevant families, and 1134 benign applications, on which the proposed method achieved an accuracy of 98.20%, precision of 97.50%, recall of 98.76%, and F-measure of 98.12%. Furthermore, we refined a testing methodology for a particular case of zero-day ransomware attacks detection—the detection of new ransomware families—by adding an adequate amount of randomly selected benign applications to the test set, providing representative evaluation performance metrics. These results represent an improvement over the performance of the current methods reported in the literature. Our advantageous approach can be applied as a technique for ransomware detection to protect computer systems from cyber threats. © 2023 Elsevier Ltd

An Ingenious Method to Detect COVID in X-Ray Images Using Machine Learning Techniques

Covid-19 is a fatal disease caused by the Covid-19 virus. It is very big problem for the whole world. The World Health Organization (WHO) has declared a pandemic. In May 2020, more people throughout the world had a favorable experience. The COVID illness is rapidly growing, and we are unable to stop it. We addressed the COVID-19 data science research initiatives employing a number of approaches, including statics, machine learning (ML), modelling, simulation, data visualization, and artificial intelligence (AI). We all suffering from COVID-19. in this case higher value of case comes from negative and lower false positive rate. The global impact of the COVID-19 outbreak was enormous. To tackle the pandemic, many projects have been launched, including those in the field of deep learning. This paper proposes a deep neural network modification based on the Xception model. The model is used to detect COVID-19 using chest X-ray images. Batch normalization and two stacks of two dense layers each are used in the proposed model. The layer addition is intended to avoid overfitting the proposed model. The proposed as a result, we compare the model's loss, accuracy, and performance speed, and the results show that the quality of the machine learning model has higher prediction accuracy and loss, but it takes longer to execute than traditional machine learning languages. Machine learning algorithms in general, and convolutional neural networks (CNNs) in particular, have shown promise in medical picture analysis and categorization. The architecture of this study has been presented for the diagnosis of COVID-19. © 2022 IEEE.

AI-based wavelet and stacked deep learning architecture for detecting coronavirus (COVID-19) from chest X-ray images.

A novel coronavirus (COVID-19), belonging to a family of severe acute respiratory syndrome coronavirus 2 (SARs-CoV-2), was identified in Wuhan city, Hubei, China, in November 2019. The disease had already infected more than 681.529665 million people as of March 13, 2023. Hence, early detection and diagnosis of COVID-19 are essential. For this purpose, radiologists use medical images such as X-ray and computed tomography (CT) images for the diagnosis of COVID-19. It is very difficult for researchers to help radiologists to do automatic diagnoses by using traditional image processing methods. Therefore, a novel artificial intelligence (AI)-based deep learning model to detect COVID-19 from chest X-ray images is proposed. The proposed work uses a wavelet and stacked deep learning architecture (ResNet50, VGG19, Xception, and DarkNet19) named WavStaCovNet-19 to detect COVID-19 from chest X-ray images automatically. The proposed work has been tested on two publicly available datasets and achieved an accuracy of 94.24% and 96.10% on 4 classes and 3 classes, respectively. From the experimental results, we believe that the proposed work can surely be useful in the healthcare domain to detect COVID-19 with less time and cost, and with higher accuracy.

Deep learning hybrid for X-ray lung disease detection

The lung diseases will cause a significant negative effect on the human lungs in a severe manner. A person may suffer from this disease because of bacteria or viruses. The alveoli in the lungs, which are a portion of the lungs that are filled fluids, so the patients with Pneumonia have a low percentage of oxygen in their blood. According to the UNICEF survey, it killed about 880,000 children belonging to the age-group of 0-5 in the year of 2016. Due to the improper detection of the infection in the starting stage, the death rate of the persons increasing enormously. Lung diseases can be detected by radiologists by looking at or examining the chest x-rays very keenly. This process of examining is very costly and requires time. To reduce the time and increase the accuracy of detection, it is needed to prevent the intervention of man from examining the chest x-rays. It is a great idea to use the convolutional neural networks, which includes in the class of deep learning, for the detection of lung diseases. It works on extracting of features from chest x-rays which classifies them to detect lung diseases. Pre-defined architectures of CNNs, which are the state-of-The-Art algorithm and techniques of transfer learning is used in the project. In this study, a Transfer Learning strategy is utilized, in which a previously trained model is utilized to train on images of various lung disorders taken from the dataset, covering safe samples. Some examples of these lung diseases are lung opacity, viral pneumonia, and covid. © 2022 IEEE.

Pre-Trained Xception Model-based COVID Detection using CXR Images

Providing the essential medical resources for COVID-19 diagnosis is a challenge on a worldwide scale. They should be cutting-edge tools that can rapidly identify and analyze the virus using a sequence of tests, and it should also be reasonably priced. A chest X-ray scan is an excellent screening tool, but if several exams are taken, the images produced by the devices must be reviewed swiftly and accurately. Predicting the progression of COVID-19-induced longitudinal lung parenchymal ground glass and the resulting consolidation of pulmonary opacity is highly challenging. Sometimes, COVID-19 will cause pulmonary opacity to consolidate, giving it a rounded appearance and a distribution on the periphery of the lungs. This study introduces the Xception model for predicting COVID-19 in chest x-rays. Chest x-rays may predict the presence of Covid-19 with an accuracy of around 97.83%. © 2022 IEEE

Transfer learning-based CNN diagnostic framework for diagnosis of COVID-19 from lung CT images

COVID-19 is an infectious disease that affects the respiratory system. To assist the physician in diagnosing lung disorders from chest CT images various systems have been developed and used. Detection of COVID-19 remains a challenging area of research. The objective of the work is to develop an inductive parameter-transfer learning-based approach for the prediction of COVID-19, pneumonia, from lung CT images. Our proposed approach is built on layer wise and convolution block-wise fine-tuning which designs the CNN architecture highly specific to lung CT image. We implemented the DenseNet201, InceptionV3, Xception, VGG19, and ResNet50 as baseline models. The network architectures are developed to learn feature representation of lung CT images. For the experimental analysis, five datasets are used. From the experimental results, it is inferred that the DenseNet201 model yields higher accuracy of 0.94 for Adam optimizer and 0.93 for the RMSprop optimizer compared to other models. © 2023 The Royal Photographic Society.

Optimization of CNN for Diagnosis on Lung Disease by Lung Segmentation and Rib Suppression

The discrimination of lung diseases by chest X- ray images is a clinically important tool. How to use artificial intelligence to accurately and quickly help doctors to diagnose different lung diseases is very important in the context of the current COVID-19 global pandemic. In this paper, we propose a model structure, including two U-Net, which implement lung segmentation and rib suppression for chest X-ray images respectively, image enhancement techniques such as histogram equalization, which enhances images contrast, and a Xception- based CNN, which classifies the processed images finally. The model can effectively avoid the interference of regions outside the lung to CNN for feature recognition and the influence of environmental factors such as X-ray machines on the quality of X-ray images and thus on the classification. The experimental results show that the classification accuracy of the model is higher than that of the direct use of the Xception model for classification. © 2022 IEEE.

Diagnosis of Covid-19 using Chest X-ray Images using Ensemble Model

The Coronavirus pandemic devastatingly affects worldwide social prosperity, and general well-being, deadening the human way of life all around the world and undermining our security. Due to the increasing number of confirmed cases associated with COVID-19, it is more important to identify the healthy and infected patients so the control of spread and treatment of infected patients can be done effectively. This work aims to correlate the presence of Covid-19 with the help of both chest X-ray images and CT Scan Images. Deep ensemble learning models take advantage of the different deep learning models, combine them, and produce a model with better performance. The proposed system involves Data augmentation and preprocessing of CT scan images. The same process is applied for Chest X-ray Images, compares the evaluation metrics amongst the models, and suggests the best use of CT scan and Chest X-ray for better Results and accuracy. The features extracted from the Inception V3 model are combined with the features extracted from the Xception model. The inception model convolves the same input tensor with the help of multiple filters, and the results are concatenated. The pre-trained Xception model is capable of depth-wise separable convolutions. The proposed framework works in Covid-19 diagnosis with an accuracy of 96% in Xception and 98% while combining Xception and InceptionV3 models. The final results showed that the Convolutional Neural Network Classifier built with the ensemble of Inception and Xception models that use X-ray images efficiently collects the essential features related to the infections of COVID-19. © 2023 IETE.

Deep Learning-Driven Medical Imaging Analysis for COVID-19 Detection

The primary requirement for early detection of COVID and control of the virus's spread is rapid and precise diagnosis. Computer vision and deep learning-based models can be used to assist this COVID diagnosis process through chest X-ray scans. This study performs a comparative analysis on different deep learning models which can be used to diagnose COVID from chest X-ray scans. For this work, the following deep learning models were selected: VGG16, Xception, ResNet, DenseNet, and MobileNet. This research looks not only at COVID, but also at other SARS-CoV-2-related diseases such as SARS and MERS. The dataset used consists of five categories: normal, COVID, pneumonia, SARS, and MERS. The comparative study showed that both the MobileNet and DenseNet models were able to deliver the best performance, with the highest accuracy and minimal loss. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Toward masked face recognition: An effective facial feature extraction and refinement model in multiple scenes

With the impact of the COVID‐19 epidemic, the demand for masked face recognition technology has increased. In the process of masked face recognition, some problems such as less feature information and poor robustness to the environment are obvious. The current masked face recognition model is not quantified enough for feature extraction, there are large errors for faces with high similarity, and the categories cannot be clustered during the detection process, resulting in poor classification of masks, which cannot be well adapted to changes in multiple environments. To solve current problems, this paper designs a new masked face recognition model, taking improved Single Shot Multibox Detector (SSD) model as a face detector, and replaces the input layer VGG16 of SSD with Deep Residual Network (ResNet) to increase the receptive field. In order to better adapt to the network, we adjust the convolution kernel size of ResNet. In addition, we fine‐tune the Xception network by designing a new fully connected layer, and reduce the training cycle. The weights of the three input samples including anchor, positive and negative are shared and clustered together with triplet network to improve recognition accuracy. Meanwhile, this paper adjusts alpha parameter in triplet loss. A higher value of alpha can improve the accuracy of model recognition. We further adopt a small trick to classify and predict face feature vectors using multi‐layer perceptron (MLP), and a total of 60 neural nodes are set in the three neural layers of MLP to get higher classification accuracy. Moreover, three datasets of MFDD, RMFRD and SMFRD are fused to obtain high‐quality images in different scenes, and we also add data augmentation and face alignment methods for processing, effectively reducing the interference of the external environment in the process of model recognition. According to the experimental results, the accuracy of masked face recognition reaches 98.3%, it achieves better results compared with other mainstream models. In addition, the hyper‐parameters tuning experiment is carried out to improve the utilization of computing resources, which shows better results than the indicators of different networks.

Face Mask Detection Using Image Processing and Convolutional Neural Networks

Face masks have become a crucial part of our everyday lives as the number of COVID cases around the world has increased, and new varieties appear every few months. We must wear a mask every time we walk outside, so the installation of face mask detectors in public places has become quite significant. In this study, we use image processing to create a face mask detection system on cascading multiple architectures of convolutional neural networks (CNN) and deep neural networks (DNN), with investigate the findings using MobileNetV2, Xception and ResNet152V2 models. The proposed technique was able to obtain excellent accuracy by training the system with CNN and DNN architectures. © 2022 IEEE.

COVID Detection using Deep Learning

COVID-19 is a rapidly spreading pandemic, with the first cases being discovered in December 2019 Wuhan, China. CT scan images of the patient's lung are used where CNN algorithm is implemented. A comparative study of two more CNN models are used to evaluate this model (Resnet). The proposed model (Resnet) is capable of accurately predicting illness with an accuracy of 95.74%. This model can distinguish between covid, pneumonia, and normal CT scan pictures. Alexnet, Resnet, and Xception methods are utilised to compare the trained model to the input photos. Its then used to forecast the outcome. COVID/PNEUMONIA will be informed to the user through SMS based on CT scan findings. Result, availability of beds in the users' immediate vicinity, and hospital recommendations will be sent as an sms to the user. © 2022 IEEE.

Convolutional Neural Network based COVID-19 Recognition from X-Ray Image

To halt extreme spread of Coronavirus(COVID-19), proper detection is the need of the hour. The number of physicians is negligible to serve the immense number of COVID-19 affected patients. For this reason, it is essential to automate the detection system of COVID-19 disease. In this proposed work, a convolutional neural network (CNN) based COVID-19 diagnosis system is developed to automate COVID- 19 disease detection using images of chest X-rays. The proposed model can differentiate three varieties: COVID-19, pneumonia and normal(healthy) from the X-ray images. Experimental process has been performed upon two publicly available datasets: COVID-19 Radiography Database and COVID-5K. Five deep convolutional neural network architectures: Xception, ResNet-50, Inception-v1, Inception-v2, and Inception-v3 are discretely used to train the system. The evaluation of the proposed system proves that Xception has provided the best performance with 99.47% accuracy, 99.21% sensitivity, 99.60% specificity, and 99.21% F1- score. The resultant of the experiment illustrates an improvement in the performance compared to some existing research works. © 2022 IEEE.

A novel deep neural network model based Xception and genetic algorithm for detection of COVID-19 from X-ray images.

The coronavirus first appeared in China in 2019, and the World Health Organization (WHO) named it COVID-19. Then WHO announced this illness as a worldwide pandemic in March 2020. The number of cases, infections, and fatalities varied considerably worldwide. Because the main characteristic of COVID-19 is its rapid spread, doctors and specialists generally use PCR tests to detect the COVID-19 virus. As an alternative to PCR, X-ray images can help diagnose illness using artificial intelligence (AI). In medicine, AI is commonly employed. Convolutional neural networks (CNN) and deep learning models make it simple to extract information from images. Several options exist when creating a deep CNN. The possibilities include network depth, layer count, layer type, and parameters. In this paper, a novel Xception-based neural network is discovered using the genetic algorithm (GA). GA finds better alternative networks and parameters during iterations. The best network discovered with GA is tested on a COVID-19 X-ray image dataset. The results are compared with other networks and the results of papers in the literature. The novel network of this paper gives more successful results. The accuracy results are 0.996, 0.989, and 0.924 for two-class, three-class, and four-class datasets, respectively.

Deep Learning-Driven Medical Imaging Analysis for COVID-19 Detection

The primary requirement for early detection of COVID and control of the virus’s spread is rapid and precise diagnosis. Computer vision and deep learning-based models can be used to assist this COVID diagnosis process through chest X-ray scans. This study performs a comparative analysis on different deep learning models which can be used to diagnose COVID from chest X-ray scans. For this work, the following deep learning models were selected: VGG16, Xception, ResNet, DenseNet, and MobileNet. This research looks not only at COVID, but also at other SARS-CoV-2-related diseases such as SARS and MERS. The dataset used consists of five categories: normal, COVID, pneumonia, SARS, and MERS. The comparative study showed that both the MobileNet and DenseNet models were able to deliver the best performance, with the highest accuracy and minimal loss. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A NOVEL COMPARATIVE STUDY FOR AUTOMATIC THREE-CLASS AND FOUR-CLASS COVID-19 CLASSIFICATION ON X-RAY IMAGES USING DEEP LEARNING

The contagiousness rate of the COVID-19 virus, which was evaluated to have been transmitted from an animal to a human during the last months of 2019, is higher than the MERS-Cov and SARS-Cov viruses originating from the same family. The high rate of contagion has caused the COVID-19 virus to spread rapidly to all countries of the world. It is of great importance to be able to detect cases quickly in order to control the spread of the COVID-19 virus. Therefore, the development of systems that make automatic COVID-19 diagnoses using artificial intelligence approaches based on X-ray, CT scans, and ultrasound images are an urgent and indispensable requirement. In order to increase the number of X-ray images used within the study, a mixed data set was created by combining eight different data sets, thus maximizing the scope of the study. In the study, a total of 9,667 X-ray images were used, including 3,405 of COVID-19 samples, 2,780 of bacterial pneumonia samples, 1,493 of viral pneumonia samples and 1,989 of healthy samples. In this study, which aims to diagnose COVID-19 disease using X-ray images, automatic classification has been performed using two different classification structures: COVID-19 Pneumonia/Other Pneumonia/Healthy and COVID-19 Pneumonia/Bacterial Pneumonia/Viral Pneumonia/Healthy. Convolutional Neural Networks (CNNs), a successful deep learning method, were used as a classifier within the study. A total of seven CNN architectures were used: Mobilenetv2, Resnet101, Googlenet, Xception, Densenet201, Efficientnetb0, and Inceptionv3 architectures. The classification results were obtained from the original X-ray images, and the images were obtained by using Local Binary Pattern and Local Entropy. Then, new classification results were calculated from the obtained results using a pipeline algorithm. Detailed results were obtained to meet the scope of the study. According to the results of the experiments carried out, the three most successful CNN architectures for both three-class and four-class automatic classification were Densenet201, Xception, and Inceptionv3, respectively. In addition, it is understood that the pipeline algorithm used in the study is very useful for improving the results. The study results show that up to an improvement of 1.57% were achieved in some comparison parameters. © 2022, Malaysian Journal of Computer Science. All Rights Reserved.