Diagnosis of COVID-19 from the X-Ray images using BAT Algorithm with Deep Convolutional Neural Network

The COVID-19 widespread has posed a chief contest to the scientific community around the world. For patients with COVID-19 illness, the international community is working to uncover, implement, or invent new approaches for diagnosis and action. A opposite transcription-polymerase chain reaction is currently a reliable tactic for diagnosing infected people. This is a time- and money-consuming procedure. Consequently, the development of new methods is critical. Using X-ray images of the lungs, this research article developed three stages for detecting and diagnosing COVID-19 patients. The median filtering is used to remove the unwanted noised during pre-processing stage. Then, Otsu thresholding technique is used for segmenting the affected regions, where Spider Monkey Optimization (SMO) is used to select the optimal threshold. Finally, the optimized Deep Convolutional Neural Network (DCNN) is used for final classification. The benchmark COVID dataset and balanced COVIDcxr dataset are used to test projected model's performance in this study. Classification of the results shows that the optimized DCNN architecture outperforms the other pre-trained techniques with an accuracy of 95.69% and a specificity of 96.24% and sensitivity of 94.76%. To identify infected lung tissue in images, here SMO-Otsu thresholding technique is used during the segmentation stage and achieved 95.60% of sensitivity and 95.8% of specificity. © 2023 IEEE.

Classifying Chest X-Ray Images using Residual Neural Network

This unprecedented time of the COVID-19 outbreak challenged the status-quo whether it is on business operation, political leadership, scientific capability, engineering implementation, data analysis, and strategic thinking, in terms of resiliency, agility, and innovativeness. Due to some identified constraints, while addressing the issue of global health, human ingenuity has proven again that in times of crisis, it is our best asset. Constraints like limited testing capacity and lack of real-time information regarding the spread of the virus, are the highest priority in the mitigation process, aside from the development of vaccines and the pushing through of vaccination programs. Using the available Chest X-Ray Images dataset and an AI-Computer Vision Technique called Convolutional Neural Network, features of the images were extracted and classified as COVID-19 positive or not. This paper proposes the usage of the 18-layer Residual Neural Network (ResNet-18) as an architecture instead of other ResNet with a higher number of layers. The researcher achieves the highest validation accuracy of 99.26%. Moving forward, using this lower number of layers in training a model classifier, resolves the issue of device constraints such as storage capacity and computing resources while still assuring highly accurate outputs. © 2022 IEEE.

Res-CovNet: an internet of medical health things driven COVID-19 framework using transfer learning.

Major countries are globally facing difficult situations due to this pandemic disease, COVID-19. There are high chances of getting false positives and false negatives identifying the COVID-19 symptoms through existing medical practices such as PCR (polymerase chain reaction) and RT-PCR (reverse transcription-polymerase chain reaction). It might lead to a community spread of the disease. The alternative of these tests can be CT (Computer Tomography) imaging or X-rays of the lungs to identify the patient with COVID-19 symptoms more accurately. Furthermore, by using feasible and usable technology to automate the identification of COVID-19, the facilities can be improved. This notion became the basic framework, Res-CovNet, of the implemented methodology, a hybrid methodology to bring different platforms into a single platform. This basic framework is incorporated into IoMT based framework, a web-based service to identify and classify various forms of pneumonia or COVID-19 utilizing chest X-ray images. For the front end, the.NET framework along with C# language was utilized, MongoDB was utilized for the storage aspect, Res-CovNet was utilized for the processing aspect. Deep learning combined with the notion forms a comprehensive implementation of the framework, Res-CovNet, to classify the COVID-19 affected patients from pneumonia-affected patients as both lung imaging looks similar to the naked eye. The implemented framework, Res-CovNet, developed with the technique, transfer learning in which ResNet-50 used as a pre-trained model and then extended with classification layers. The work implemented using the data of X-ray images collected from the various trustable sources that include cases such as normal, bacterial pneumonia, viral pneumonia, and COVID-19, with the overall size of the data is about 5856. The accuracy of the model implemented is about 98.4% in identifying COVID-19 against the normal cases. The accuracy of the model is about 96.2% in the case of identifying COVID-19 against all other cases, as mentioned.

A comprehensive review of analyzing the chest X-ray images to detect COVID-19 infections using deep learning techniques.

COVID-19, a highly infectious respiratory disease a used by SARS virus, has killed millions of people across many countries. To enhance quick and accurate diagnosis of COVID-19, chest X-ray (CXR) imaging methods were commonly utilized. Identifying the infection manually by radio imaging, on the other hand, was considered, extremely difficult due to the time commitment and significant risk of human error. Emerging artificial intelligence (AI) techniques promised exploration in the development of precise and as well as automated COVID-19 detection tools. Convolution neural networks (CNN), a well performing deep learning strategy tends to gain substantial favors among AI approaches for COVID-19 classification. The preprints and published studies to diagnose COVID-19 with CXR pictures using CNN and other deep learning methodologies are reviewed and critically assessed in this research. This study focused on the methodology, algorithms, and preprocessing techniques used in various deep learning architectures, as well as datasets and performance studies of several deep learning architectures used in prediction and diagnosis. Our research concludes with a list of future research directions in COVID-19 imaging categorization.

Detection of Pneumonia from Chest X-ray Images Utilizing MobileNet Model.

Pneumonia has been directly responsible for a huge number of deaths all across the globe. Pneumonia shares visual features with other respiratory diseases, such as tuberculosis, which can make it difficult to distinguish between them. Moreover, there is significant variability in the way chest X-ray images are acquired and processed, which can impact the quality and consistency of the images. This can make it challenging to develop robust algorithms that can accurately identify pneumonia in all types of images. Hence, there is a need to develop robust, data-driven algorithms that are trained on large, high-quality datasets and validated using a range of imaging techniques and expert radiological analysis. In this research, a deep-learning-based model is demonstrated for differentiating between normal and severe cases of pneumonia. This complete proposed system has a total of eight pre-trained models, namely, ResNet50, ResNet152V2, DenseNet121, DenseNet201, Xception, VGG16, EfficientNet, and MobileNet. These eight pre-trained models were simulated on two datasets having 5856 images and 112,120 images of chest X-rays. The best accuracy is obtained on the MobileNet model with values of 94.23% and 93.75% on two different datasets. Key hyperparameters including batch sizes, number of epochs, and different optimizers have all been considered during comparative interpretation of these models to determine the most appropriate model.

Explicable AI for surveillance and interpretation of Coronavirus using X-ray imaging

Explainable AI (XAI) is one of the disciplines being investigated, with the goal of improving the transparency of black-box systems. XAI is such a technology that could assist to alleviate the black-box system by providing new ways of understanding the core thinking process of AI systems. Conside ring the healthcare domain, doctors are still not able to explain why certain decisions or forecasts had been predicted by a particular system. As a result, it imposes limitations on how and where AI technology can be implemented. And to address this problem, a taxonomy of model interpretability is framed for conceptualizing the explainability. Also, an approach with the baseline system is created which could firstly differentiate in the Covid-19 positive and Covid-19 negative chest X-ray images and an automated explainable pipeline is designed using XAI technique. This technique shows that the model is interpretable, that is the achieved results are easy to understand and can encourage medicians and patients with transparent and reliable medical journey. This article aims to help people comprehend the necessity for Explainable AI, as well as the methodological approaches used in healthcare. © 2023 IEEE.

Machine Learning Algorithm for Trend Analysis in Short term Forecasting of COVID-19 using Lung X-ray Images

With the development of medical technology, the diagnosis of lung diseases relies more on the determination of medical images. With increasingly huge data, a powerful data processing model is urgently needed to provide favorable support for this field. The goal of this study is to develop a computer-assisted method to identify COVID-19 from X-ray pictures of the lungs at the very beginning of the disease. The architecture is implemented as a software system on a computer that can assist in the affordable and accurate early identification of cardiac illness. The performance of CNN architecture is best among all other classification algorithms to detect COVID-9 from Lung X-ray images. The datasets consist of COVID-19 established cases for 4 weeks which included the X-ray images of the chest. Then the distribution of the data was examined according to the statistical distribution. For this prediction, time series models are used for forecasting the pandemic situation. The performances of the methods were compared according to the MSE metric and it was seen that the Convolutional Neural Networks (CNN) achieved the optimal trend pattern.

A COVID-19 Detection Model Based on Convolutional Neural Network and Residual Learning

A model that can obtain rapid and accurate detection of coronavirus disease 2019 (COVID-19) plays a significant role in treating and preventing the spread of disease transmission. However, designing such a model that can balance the detection accuracy and weight parameters of memory well to deploy a mobile device is challenging. Taking this point into account, this paper fuses the convolutional neural network and residual learning operations to build a multi-class classification model, which improves COVID-19 pneumonia detection performance and keeps a trade-off between the weight parameters and accuracy. The convolutional neural network can extract the COVID-19 feature information by repeated convolutional operations. The residual learning operations alleviate the gradient problems caused by stacking convolutional layers and enhance the ability of feature extraction. The ability further enables the proposed model to acquire effective feature information at a low cost, which can make our model keep small weight parameters. Extensive validation and comparison with other models of COVID-19 pneumonia detection on the well-known COVIDx dataset show that (1) the sensitivity of COVID-19 pneumonia detection is improved from 88.2% (non-COVID-19) and 77.5% (COVID-19) to 95.3% (non-COVID-19) and 96.5% (COVID-19), respectively. The positive predictive value is also respectively increased from 72.8% (non-COVID-19) and 89.0% (COVID-19) to 88.8% (non-COVID-19) and 95.1% (COVID-19). (2) Compared with the weight parameters of the COVIDNet-small network, the value of the proposed model is 13 M, which is slightly higher than that (11.37 M) of the COVIDNet-small network. But, the corresponding accuracy is improved from 85.2% to 93.0%. The above results illustrate the proposed model can gain an efficient balance between accuracy and weight parameters. © 2023 Tech Science Press. All rights reserved.

Precision Diagnosis to Discriminate Covid-19 and Pneumonia using Mixed-data Model based on Custom Neural Networks

Classifying Covid-19 and Pneumonia is one of the most important and challenging tasks in the field of the medical sector since manual classification with human assistance can lead to incorrect prediction and diagnosis. Additionally, it is a difficult operation when there is a lot of data that need to be analyzed thoroughly. Due to the similarity in symptoms as well as in chest X-ray images of Covid-19 and Pneumonia diseases, it is difficult to distinguish those. The study presents a technological solution to build a mixed-data model using customized neural networks to discriminate between Covid-19 and Pneumonia. The proposed method is applied to the chest X-ray images and symptoms of patients of Covid-19 and Pneumonia. This helps to perform immediate prediction of Covid-19 and Pneumonia providing fast and specialized treatment to the patients appropriately. This prediction also helps the radiologist or doctors in making quick decisions. In this work, imaging data (such as Chest X-ray images) and text data (such as disease symptoms like cough, body pain, short breathing, fever, etc.) are taken for detecting Covid-19, Pneumonia and Normal patients. Data Synthesis is carried out due to the unavailability of mixed data and it has created dataset of 450 entries of Covid-19, Normal and Pneumonia cases. The goal is to design a system that accurately classifies Covid19, Pneumonia, and Normal patients by utilizing convolutional neural networks (CNN) and multi-layer perceptron (MLP) algorithms. An accuracy of 93.33% is obtained for the mixed-data model using a deep neural network, that is designed by combining custom CNN and MLP architectures. © 2022 IEEE.

A Lightweight AMResNet Architecture with an Attention Mechanism for Diagnosing COVID-19.

AIMS: COVID-19 has become a worldwide epidemic disease and a new challenge for all mankind. The potential advantages of chest X-ray images on COVID-19 were discovered. We proposed a lightweight and effective Convolution Neural Network framework based on chest X-ray images for the diagnosis of COVID-19, named AMResNet. BACKGROUND: COVID-19 has become a worldwide epidemic disease and a new challenge for all mankind. The potential advantages of chest X-ray images on COVID-19 were discovered. OBJECTIVE: A lightweight and effective Convolution Neural Network framework based on chest X-ray images for the diagnosis of COVID-19. METHOD: By introducing the channel attention mechanism and image spatial information attention mechanism, a better level can be achieved without increasing the number of model parameters. RESULT: In the collected data sets, we achieved an average accuracy rate of more than 92%, and the sensitivity and specificity of specific disease categories were also above 90%. CONCLUSION: The convolution neural network framework can be used as a novel method for artificial intelligence to diagnose COVID-19 or other diseases based on medical images.

Application of Convolutional Neural Networks for COVID-19 Detection in X-ray Images Using InceptionV3 and U-Net.

COVID-19 has expanded overall across the globe after its initial cases were discovered in December 2019 in Wuhan-China. Because the virus has impacted people's health worldwide, its fast identification is essential for preventing disease spread and reducing mortality rates. The reverse transcription polymerase chain reaction (RT-PCR) is the primary leading method for detecting COVID-19 disease; it has high costs and long turnaround times. Hence, quick and easy-to-use innovative diagnostic instruments are required. According to a new study, COVID-19 is linked to discoveries in chest X-ray pictures. The suggested approach includes a stage of pre-processing with lung segmentation, removing the surroundings that do not provide information pertinent to the task and may result in biased results. The InceptionV3 and U-Net deep learning models used in this work process the X-ray photo and classifies them as COVID-19 negative or positive. The CNN model that uses a transfer learning approach was trained. Finally, the findings are analyzed and interpreted through different examples. The obtained COVID-19 detection accuracy is around 99% for the best models.

Automated Detection of Covid-19 from X-ray Using SVM

Earlier discovery of COVID-19 through precise diagnosis, particularly in instances with no evident symptoms, may reduce the mortality rate of patients. Chest X-ray images are the primary diagnostic tool for this condition. Patients exhibiting COVID-19 symptoms are causing hospitals to become overcrowded, which is becoming a big concern. The contribution that machine learning has made to big data medical research has been very helpful, opening up new ways to diagnose diseases. This study has developed a machine vision method to identify COVID-19 using X-ray images. The preprocessing stage has been applied to resize images and enhance the quality of X-ray images. The Gray-level co-occurrence matrix (GLCM) and Gray-Level Run Length Matrix (GLRLM) are then used to extract features from the X-ray images, and these features are combined to develop the performance classification via training by Support Vector Machine (SVM). The testing phase evaluated the model's performance using generalized data. This developed feature combination utilizing the GLCM and GLRLM algorithms assured a satisfactory evaluation performance based on COVID-19 detection compared to the immediate, single feature with a testing accuracy of 96.65%, a specificity of 99.54%, and a sensitivity of 97.98%. © 2022 IEEE.

A Deep Learning Network for Detecting COVID-19 From Chest X-Ray Images

Rapid and accurate detection of COVID-19 plays a significant role in treating and preventing the spread of disease transmission. To this end, we fuse the convolutional neural network and residual learning operation to build a multi-class classification model, which has a few parameters and is more conducive to be deployed on a mobile device. Extensive experiments show that our proposed model gains competitive performance. Compared with the COVIDNet-small network, the sensitivity of COVID-19 pneumonia detection is improved from 88.2% (non-COVID-19) and 77.5% (COVID-19) to 95.3% (non-COVID-19) and 96.5% (COVID-19). Alternatively, the Positive predictive value is increased from 72.8% (non-COVID-19) and 89.0% (COVID-19) to 88.8% (non-COVID-19) and 95.1 % (COVID-19). The accuracy is also improved from 85.2 % to 93.0 %, which is very close to the value (93.3 %) of the COVIDNet-large network. But, the weight parameters (13M) of the proposed model are slightly higher than that (11.37M) of the COVIDNet-small network, but only about one-third of that (37.85M) of the COVIDNet-large network. © 2022 IEEE.

An Effective Deep Learning Model to Discriminate Coronavirus Disease From Typical Pneumonia

Current technological advances are paving the way for technologies based on deep learning to be utilized in the majority of life fields. The effectiveness of these technologies has led them to be utilized in the medical field to classify and detect different diseases. Recently, the pandemic of coronavirus disease (COVID-19) has imposed considerable press on the health infrastructures all over the world. The reliable and early diagnosis of COVID-19-infected patients is crucial to limit and prevent its outbreak. COVID-19 diagnosis is feasible by utilizing reverse transcript-polymerase chain reaction testing;however, diagnosis utilizing chest x-ray radiography is deemed safe, reliable, and precise in various cases. © 2022 IGI Global. All rights reserved.

A Neutrosophic Set Approach on Chest X-rays for Automatic Lung Infection Detection

COVID cases and its variants is noted enormously in the past three years. In many medical cases, lung infections such as viral pneumonia, bacterial pneumonia have been initially interpreted as COVID-19. Hence, the proposed work is concentrating on differentiating these lung infection types. This work focuses on using neutrosophic approach of classifying into True (T), False (F) and Indeterminacy (I) set membership to reduce the fuzziness and retain more significant information for feature extraction of the opacity to differenti-ate the types of lung infections. Initially, the images are preprocessed by alpha-mean and beta-enhancement operation to reduce the indeterminacy and enhancing the image components as the range of lung opacity levels to determine the types. Then, these neutrosophic set enhanced images are fed to various deep learning models like ResNet-50, VGG-16 and XGBoost for classification. Experiments are conducted on ActualMed COVID-19 Chest X-ray and COVID-19 radiography dataset and a comparative analysis on several domain set of images such as the original image, neutrosophic domain (T, I, F) and enhanced neutrosophic domain (alpha, beta) are trained and tested through transfer learning by tuning the various validation parameters. On experimental analysis, an enhanced neutrosophic image achieves a better accuracy of 97.33% among the other domain sets. © 2023, Kauno Technologijos Universitetas. All rights reserved.

Classification of COVID-19 Cases from X-Ray Images Based on a Modified VGG-16 Model

COVID-19 is considered one of the most deadly pandemics by the World Health Organization and has claimed the lives of millions around the world. Mechanisms for early diagnosis and detection of this rapidly spreading disease are necessary to save lives. However, the increase in COVID-19 cases requires not relying on traditional means of detecting diseases due to these tests' limitations and high costs. One diagnostic technique for COVID-19 is X-rays and CT scans. For accurate and highly efficient diagnosis, computer-aided diagnosis is required. In this research, we suggest a convolutional neural network for chest x-ray images categorisation into two classes of infection: COVID-19 and normal. The suggested model uses an upgraded model based on the VGG-16 architecture that has been trained end-to-end on a dataset composed of X-ray images obtained from two different public data repositories, which include 1,320 and 1,578 cases in the COVID-19 and normal classes, respectively. This suggested model was trained and evaluated on the provided dataset and showed that our proposed model showed improved performance in the matter of overall accuracy, recall, precision, and F1-score at 99.54%, 99.5%, 99.5%, and 99.5%, respectively. The system's significance is supported because it has greater accuracy than other contemporary deep learning methods in the literature on COVID-19 identification.

Wavelet and modified VGG-16 deep learning to identify COVID-19 based on X-ray images

COVID-19 is caused by the SARS coronavirus 2 family (SARS-CoV-2). A quick antibody or antigen test can detect the presence of COVID-19, but further testing is needed to confirm a positive result. Radiologists use chest X-rays to diagnose chest diseases early. The proposed system integrates discrete wavelet transformation and deep learning to help radiologists categorise conditions. Wavelets break down images into multiple spatial resolutions depending on a high pass and low pass frequency components and efficiently extract characteristics from lung X-rays. Here, we use a hybrid wavelet-CNN model to diagnose lung X-rays. The proposed CNN model is trained and verified on different source COVID 19 chest X-ray images for binary and three classes. The proposed studies suggest significant improvement in outcomes, with the best parameters achieving 99.42% accuracy and 96.43% accuracy for binary and three classes. The depiction of feature maps shows that our suggested network collected features from the corona virus-affected lung properly. Results suggest that the proposed model is successful enough for COVID 19 diagnosis. © 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.

A Lightweight CNN Architecture for Chest X-ray Images Analysis to Facilitate Early Stage Detection of Lung Diseases

Lung-related diseases are one of the significant causes of death among infants and children. However, the mortality rate can be reduced by the detection of lung abnormality at an early stage. Traditionally, radiologists identify irregularities by interpreting chest x-ray images which is time-consuming. Therefore, researchers have proposed many automated systems for diagnosing pneumonia and other lung-related diseases. Due to the remarkable performance of Convolutional Neural Networks(CNN) in image classification, it has gained immense popularity in chest x-ray image analysis. Most of the research has utilized famous pre-trained Imagenet models for more accurate analysis of Chest X-ray images. However, the problem with these architectures is that they have many parameters that increase the training time, which makes the detection process lengthy. This paper introduces a lightweight, compact, and well-tuned CNN architecture with far fewer parameters than the pre-trained model to analyze two of the most common lung diseases, pneumonia and Covid-19. We have evaluated our model on two benchmark datasets. Experimental results show that our lightweight CNN model has far fewer hyperparameters than other state-of-the-art models but achieves similar results. We have achieved an accuracy of 90.38% on the kermany dataset and 96.90% on the Covid-19 Radiography dataset. © 2022 IEEE.

An Intelligent Edge-as-a-Service Framework to Combat COVID-19 Using Deep Learning Techniques

This study proposes and develops a secured edge-assisted deep learning (DL)-based automatic COVID-19 detection framework that utilizes the cloud and edge computing assistance as a service with a 5G network and blockchain technologies. The development of artificial intelligence methods through services at the edge plays a significant role in serving many applications in different domains. Recently, some DL approaches have been proposed to successfully detect COVID-19 by analyzing chest X-ray (CXR) images in the cloud and edge computing environments. However, the existing DL methods leverage only local and small training datasets. To overcome these limitations, we employed the edges to perform three tasks. The first task was to collect data from different hospitals and send them to a global cloud to train a DL model on massive datasets. The second task was to integrate all the trained models on the cloud to detect COVID-19 cases automatically. The third task was to retrain the trained model on specific COVID-19 data locally at hospitals to improve and generalize the trained model. A feature-level fusion and reduction were adopted for model performance enhancement. Experimental results on a public CXR dataset demonstrated an improvement against recent related work, achieving the quality-of-service requirements. © 2023 by the authors.