A new modification CNN using VGG19 and ResNet50V2 for classification of COVID-19 from X-ray radiograph images

Coronavirus often called COVID-19 is a deadly viral disease that causes as a result of severe acute respiratory syndrome coronavirus-2 that needs to be identified especially at its early stages, and failure of which can lead to the further spread of the virus. Despite with the huge success recorded towards the use of the original convolutional neural networks (CNN) of deep learning models. However, their architecture needs to be modified to design their modified versions that can have more powerful feature layer extractors to improve their classification performance. This research is aimed at designing a modified CNN of a deep learning model that can be applied to interpret X-rays to classify COVID-19 cases with improved performance. Therefore, we proposed a modified convolutional neural network (shortened as modification CNN) approach that uses X-rays to classify a COVID-19 case by combining VGG19 and ResNet50V2 along with putting additional dense layers to the combined feature layer extractors. The proposed modified CNN achieved 99.24%, 98.89%, 98.90%, 99.58%, and 99.23% of the overall accuracy, precision, specificity, sensitivity, and F1-Score, respectively. This demonstrates that the results of the proposed approach show a promising classification performance in the classification of COVID-19 cases. © 2023 Institute of Advanced Engineering and Science. All rights reserved.

Computerized Face Mask Detection System Using Deep CNN and Transfer Learning

Since the COVID-19 pandemic is on the rise again with hazardous effects in China, it has become very crucial for global individuals and the authorities to avoid spreading of the virus. This research aims to identify algorithms with high accuracy and moderate computing complexity at the same time (although conventional machine learning works on low computation power, we have rather used CNN for our research work as the accuracy of CNN is drastically greater than the former), to identify the proper enforcement of face masks. In order to find the best Neural Network architecture we used many deep CNN Methodologies to solve classification problem in regards of masked and non masked image dataset. In this approach we applied different model architectures, like VGG16, Resnet50, Resnet101 and VGG19, on a large dataset to train on and compared the model on the basis of accuracy in which VGG16 came out to be the best. VGG16 was further tuned with different optimizers to determine the one best fit of the model. VGG16 gave an ideal accuracy of 99.37% with the best fit optimizer over a real life data set. © 2023 IEEE.

Performance Evaluation of Learning Models for the Prognosis of COVID-19.

COVID-19 has developed as a worldwide pandemic that needs ways to be detected. It is a communicable disease and is spreading widely. Deep learning and transfer learning methods have achieved promising results and performance for the detection of COVID-19. Therefore, a hybrid deep transfer learning technique has been proposed in this study to detect COVID-19 from chest X-ray images. The work done previously contains a very less number of COVID-19 X-ray images. However, the dataset taken in this work is balanced with a total of 28,384 X-ray images, having 14,192 images in the COVID-19 class and 14,192 images in the normal class. Experimental evaluations were conducted using a chest X-ray dataset to test the efficacy of the proposed hybrid technique. The results clearly reveal that the proposed hybrid technique attains better performance in comparison to the existing contemporary transfer learning and deep learning techniques.

An Automated CHNN Model for the Classification and Detection of Lung Diseases using Transfer Learning

The COVID-19 pandemic began in December2019 and caused a global crisis. The WHO declared it a pandemic on March 11, 2020. Since October 10, 2020, COVID-19 has affected 200+ countries, causing over 37 million confirmed cases and 1 million deaths. RT-PCR is the usual method for detecting it, but it has drawbacks. Individuals who exhibit symptoms of COVID-19 but receive negative results from RT-PCR tests may be diagnosed with the disease using chest X-rays and CT scans, as these imaging techniques are capable of detecting lung abnormalities that are commonly associated with COVID-19, including consolidation and ground-glass opacities. The detection of COVID-19 systems faces numerous challenges, including false negatives, limited testing capacity, a scarcity of imaging equipment, and a shortage of data. With the increasing number of cases, there is a pressing need for a quicker, more cost-effective screening method. Chest X-ray scans can serve as a supplementary or confirming approach as they are fast and readily available. An Automated Hybrid Convolutional Neural network-Hopfield Neural Network (CHNN) is proposed in this study by extracting the features using VGG-19 for the classification and detection of lung diseases. In this work, both two-fold and multi-class classifications have been done with 99% and 97% accuracy respectively. © 2023 IEEE.

COVID-19 prediction with Chest X-Ray images using CNN

Coronavirus has outbreak as an epidemic disease, created a pandemic situation for the public health across the Globe. Screening for the large masses is extremely crucial to control disease for the people in a neighborhood. Real-time-PCR[18] is the general diagnostic approach for pathological examination. However, the increasing figure of false results from the test has created a way in choosing alternative procedures. COVID-19 patient's X-rays images of chest has emerged as a significant approach for screening the COVID-19 disease. However, accuracy depends on the knowledge of a radiologist. X-Ray images of lungs may be proper assistive tool for diagnosis in reducing the burden of the doctor. Deep Learning techniques, especially Convolutional Neural Networks (CNN), have been shown to be effective for classification of images in the medical field. Diagnosing the COVID-19 using the four types of Deep-CNN models because they have pre-trained weights. Model needs to pre-trained on the ImageNet database in simplifying the large datasets. CNN-based architectures were found to be ideal in diagnosing the COVID-19 disease. The model having an efficiency of 0.9835 in accuracy, precision of 0.915, sensitivity of 0.963, specificity with 0.972, 0.987 F1 Score and 0.925 ROC AUC. © 2023 IEEE.

Lung Lesion Images Classification Based on Deep Learning Model and Adaboost Techniques

Today, the medical industry is promoting the research and application of artificial intelligence in disease diagnosis and treatment. The development of diagnostic methods with the support of electronic devices and information technology can help doctors save time in diagnosing and treating diseases, especially medical images. Diagnosis of lung lesions based on lung images is a case study. This paper proposed a method for lung lesion images classification based on modified U-Net and VGG-19 combined on adaboost techniques. The modified U-Net architecture with 5 pooling and 5 unpooling. It has the unpooling layer with kernels of size 2 × 2, stride 2 × 2 to get output consistent with the adaboost. The result of the proposed method is about 97.61% and better results than others in the Covid-19 radiography dataset. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

A Deep Learning Model for Early Prediction of Pneumonia Using VGG19 and Neural Networks

Pneumonia is a disease that can be caused by bacteria, viruses, and fungi. According to WHO, pneumonia is responsible for 22% of all deaths of children under the age of 1–5 years which is one of the main causes of increased mortality rate. Congestion, gray hepatization, red hepatization, and resolution are the stages of this disease. If the disease is not detected in time, it can progress to a fatal stage. The chest X-ray image is used to diagnose pneumonia, but it requires the presence of experienced radiologists. Pneumonia, COVID-19, cancer, and various other diseases can be identified using X-ray images. If the disease is incorrectly identified, severe difficulties may arise. A deep learning-based model called VGG19 is used to address this issue, which classifies pneumonia from normal lungs. A chest X-ray dataset containing 5856 images was used in this study to classify pneumonia from normal lungs. The outcomes have been demonstrated as accuracy, precision, recall, F1-score, and receiver operating characteristics with the values of 93%, 0.931, 0.93, 0.931, and 0.973, respectively. Furthermore, for validating the proposed model, the performance parameters are compared to the existing work, which results that the proposed model outperforms the other models. In future, this work could be used in hospitals and medical applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

Comparative Analysis of Various Models for Potato Leaf Disease Classification using Deep Learning

Potato is one among the most extensively consumed staple foods, ranking fourth on the global food pyramid. Moreover, because of the global coronavirus outbreak, global potato consumption is expanding dramatically. Potato diseases, on the other hand, are the primary cause of crop quality and quantity decline. Plant conditions will be dramatically worsened by incorrect disease classification and late identification. Fortunately, leaf conditions can help identify various illnesses in potato plants. Potato (Solanum tuberosum L) is one of the majorly farmed vegetable food crops in worldwide. The output of potato crops in both quality and quantity is affected majorly due to fungal blight infections, which causes a severe impact on the global food yield. The most severe foliar diseases for potato crops are early blight and late blight. The causes of these diseases are Alternaria solani and Phytophthora infestants respectively. Farmers suspect such problems by focusing on the color change or transformation in potato leaves, which is effortless due to subjectivity and lengthy time commitment. In such circumstances, it is critical to develop computer models that can diagnose those diseases quickly and accurately, even in their early stages. © 2023 IEEE.

Detection of COVID-19 using ResNet50, VGG19, MobileNet, and Forecasting, using Logistic Regression, Prophet, and SEIRD Model

Artificial Intelligence can quickly identify hazardous viral strains in humans. To detect COVID-19 symptoms, AI algorithms can be used to train to examine medical images like X-rays and CT scans. This can help healthcare providers to diagnose the disease more accurately and quickly. AI helps examine data on the spread of COVID-19 andmake predictions about how it will likely spread in the future. Machine learning algorithms known as Convolutional Neural Networks (CNN) are highly effective at evaluating images. As a result, CNN could assist in the early detection of COVID-19 by evaluating medical images like X-rays and CT scans to spot the disease's symptoms. This article's main aim is to provide brief information on some of the CNN models to detect and forecast COVID-19. The models were purely trained with Chest X-ray images of different categorized patients. The COVID-19 prediction models like ResNet50, VGG19, and MobileNet give accuracies of 98.50%, 97.68%, and 93.94%, respectively. On the other hand, forecasting also plays a vital role in reducing the pandemic because it helps us to analyze the risk and plan a solution to avoid it. The model is trained with some forecasting techniques like Prophet, LogisticRegression, and S EIRD model based on a text-based dataset that contains parameters such as the number of people infected per day recovered per day an d many more for visualizing the trends in forecasting, which help in decision-making to analyze risks and plan solutions to prevent the further spread of the disease. © 2023 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-Based Model for Face Mask Detection in the Era of COVID-19 Pandemic

Recent advancements in the growth of classification tasks and deep learning have culminated in the worldwide success of numerous practical applications. With the onset of COVID-19 pandemic, it becomes very important to use technology to help us control the infectious nature of the virus. Deep learning and image classification can help us detect face mask from a crowd of people. However, choosing the correct deep learning architecture can be crucial in the success of such an idea. This study presents a model for extracting features from face masks utilizing pre-trained models ConvNet, InceptionV3, MobileNet, DenseNet, ResNet50, and VGG19, as well as stacking a fully connected layer to solve the issue. On the face mask 12 k dataset, the study assesses the effectiveness of the suggested deep learning approaches for the task of facemask detection. The performance metrics used for analysis are loss, accuracy, validation loss, and validation accuracy. The maximum accuracy is achieved by DenseNet and MobileNet. Both the models gave a comparable and good accuracies in terms of training and validation (99.89% and 99.79%), respectively. Further, the paper also demonstrates the deployment of deep learning architecture in the real-world using Raspberry Pi 2B (1 GB RAM). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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.

Analysis on Chest X-ray and Computed Tomography Images to Detect COVID-19 Based on Deep Learning Techniques

More than 6.3 million individuals have died as a result of the Corona Virus Disease 2019 (COVID-19), which spoiled many more human health globally. Since COVID-19 is a pandemic that is rapidly spreading, early discovery is essential to halting the infection's spread. Images of the lungs are utilised to identify coronavirus infection. For the identification of Corona Virus Disease, chest X-ray (CXR) and computed tomography (CT) images are available. Deep learning methods are proved to be effective and perform better in medical imaging applications. This study examines lung CT pictures, classifies and segments them, and uses the results to identify whether a patient tested is affected by COVID-19 or not using Deep learning techniques. The COVID detection performance of the deep learning architectures GG19, MobileNet, COVID-Net (PEPX), Squeez Net, U-Net, DarkNet and VGG16 are analysed - it was shown that U-Net combined VGG16 (acc98.89%) and VGG19 (acc-98.05%) performs the best, followed by MobileNet and QueezNet. © 2022 IEEE.

Covid-19 Identification through Chest X-ray Images using Deep Learning Classifiers

In the context of the nidovirales order, the coronavirus (Covid-19) is a virus family i.e. extracted from Ribonucleic Acid (RNA) viruses. The pandemic ensued due to it has already infected 9,716,060 people across the globe and is still causing problems with mutations of concern. Because of the immense number of infected patients, and the resulting deficiency of testing kits in hospitals;a rapid, reliable, and automatic detection system is in extreme need to curb the numbers. SARS-Cov-2 is an influenza kind of virus that can be detected using imaging techniques. It is important to distinguish between Covid-19 (caused by SARS-Cov-2) disease against pneumonia disease infected patients and healthy person's chest x-ray scans respectively. Advanced computational techniques like ML (machine learning) and DL (deep learning) had proven to be extremely useful in image processing, especially for the processing of medical images. In this work, 2906 images were taken from the publically available datasets. Various transfer learning-based DL models are applied to these images. Resulting that the ML-based classifiers effectively categorizing the input images (normal/Covid-19/pneumonia). The model achieves 96.3% accuracy with the VGG19 model and Logistic Regression (LR) classifier. This model proves to be highly convenient in treating this pandemic disease Covid-19. © 2022 IEEE.

Detection of Covid-19 using Cough Sounds

Coronavirus, the pandemic due to which about 4 million have lost their lives and counting, is still on. Many scientists and researchers are trying to find ways to detect coronavirus as soon as possible in the human body so that they can start their medication and precaution as soon as possible. Still, due to lack of lab facilities, the RT-PCR is taking more than three days to give the report, and in the meanwhile, patients get serious and life in danger. So in this paper, we proposed an audio-based coronavirus detection technique in which we can get results in minutes. Coronavirus is a respiratory disease, and the sound produced while breathing can tell us about the presence of coronavirus. Audio-based detection was already used for the detection of asthma, pneumonia. So, in this paper, we implemented a combination of machine learning and deep learning techniques to find the presence of Covid-19, and the model has an accuracy of 78% and an f1 score of 74%. This technique can be used as a starting point for just audio data to diagnose diseases and save lives. © 2022, American Scientific Publishing Group (ASPG). All rights reserved.

A Model based on Deep Learning for COVID-19 X-rays Classification

Throughout the COVID-19 pandemic in 2019 and until now, patients overrun hospitals and health care emergency units to check up on their health status. The health care systems were burdened by the increased number of patients and there was a need to speed up the diagnoses process of detecting this disease by using computer algorithms. In this paper, an integrated model based on deep and machine learning for covid-19 x-rays classification will be presented. The integration is built-up open two phases. The first phase is features extraction using deep transfer models such as Alexnet, Resnet18, VGG16, and VGG19. The second phase is the classification using machine learning algorithms such as Support Vector Machine (SVM), Decision Trees, and Ensemble algorithm. The dataset selected consists of three classes (COVID-19, Viral pneumonia, and Normal) class and the dataset is available online under the name COVID-19 Radiography database. More than 30 experiments are conducted to select the optimal integration between machine and deep learning models. The integration of VGG19 and SVM achieved the highest accuracy possible with 98.61%. The performance indicators such as Recall, Precision, and F1 Score support this finding. The proposed model consumes less time and resources in the training process if it is compared to deep transfer models. Comparative results are con-ducted at the end of the research, and the proposed model overcomes related works which used the same dataset in terms of testing accuracy. © 2023, Modern Education and Computer Science Press.

Diagnosis of Coronavirus Disease From Chest X-Ray Images Using DenseNet-169 Architecture.

The coronavirus disease (COVID-19) is a very contagious and dangerous disease that affects the human respiratory system. Early detection of this disease is very crucial to contain the further spread of the virus. In this paper, we proposed a methodology using DenseNet-169 architecture for diagnosing the disease from chest X-ray images of the patients. We used a pretrained neural network and then utilised the transfer learning method for training on our dataset. We also used Nearest-Neighbour interpolation technique for data preprocessing and Adam Optimizer at the end for optimization. Our methodology achieved 96.37 % accuracy which was better than that obtained using other deep learning models like AlexNet, ResNet-50, VGG-16, and VGG-19.

Enabling CT-Scans for covid detection using transfer learning-based neural networks.

Today, we are coping with the pandemic, and the novel virus is covertly evolving day by day. Therefore, a precautionary system to deal with the issue is required as early as possible. The last few years were very challenging for doctors, vaccine makers, hospitals, and medical authorities to deal with the massive crowd to provide results for all patients and newcomers in the past months. Thus, these issues should be handled with a robust system that can accord with many people and deliver the results in a fraction of time without visiting public places and help reduce crowd gathering. So, to deal with these issues, we developed an AI model using transfer learning that can aid doctors and other people to get to know whether they were suffering from covid or not. In this paper, we have used VGG-19 (CNN-based) model with open-sourced COVID-CT (CTSI) dataset. The dataset consists of 349 images of COVID-19 of 216 patients and 463 images of NON-COVID-19. We have achieved an accuracy of 95%, precision of 96%, recall of 94%, and F1-Score of 96% from the experiments.Communicated by Ramaswamy H. Sarma.

A deep learning architecture for multi-class lung diseases classification using chest X-ray (CXR) images

In 2019, the world experienced the rapid outbreak of the Covid-19 pandemic creating an alarming situation worldwide. The virus targets the respiratory system causing pneumonia with other symptoms such as fatigue, dry cough, and fever which can be mistakenly diagnosed as pneu-monia, lung cancer, or TB. Thus, the early diagnosis of COVID-19 is critical since the disease can provoke patients' mortality. Chest X-ray (CXR) is commonly employed in healthcare sector where both quick and precise diagnosis can be supplied. Deep learning algorithms have proved extraordi-nary capabilities in terms of lung diseases detection and classification. They facilitate and expedite the diagnosis process and save time for the medical practitioners. In this paper, a deep learning (DL) architecture for multi-class classification of Pneumonia, Lung Cancer, tuberculosis (TB), Lung Opacity, and most recently COVID-19 is proposed. Tremendous CXR images of 3615 COVID-19, 6012 Lung opacity, 5870 Pneumonia, 20,000 lung cancer, 1400 tuberculosis, and 10,192 normal images were resized, normalized, and randomly split to fit the DL requirements. In terms of classification, we utilized a pre-trained model, VGG19 followed by three blocks of con-volutional neural network (CNN) as a feature extraction and fully connected network at the clas-sification stage. The experimental results revealed that our proposed VGG19 + CNN outperformed other existing work with 96.48 % accuracy, 93.75 % recall, 97.56 % precision, 95.62 % F1 score, and 99.82 % area under the curve (AUC). The proposed model delivered supe-rior performance allowing healthcare practitioners to diagnose and treat patients more quickly and efficiently.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/ 4.0/).

COVID-19 & Lung Disease Detection using Deep Learning

Corona virus disease 2019 (COVID-19) is an infectious disease. We have proposed a COVID-19 disease detection using deep learning method in this paper. Novel disease coronavirus bring forth diverse effect on population. Exponential growth of virus and lack of knowledge of treatment was the biggest challenge for doctors to save patient's life. Due to less availability of ventilator and ICU clinical trial and testing overloaded of COVID-19 health status. Lung infection diagnosed by Chest X-ray found as best and fastest approach to detect severity of COVID-19. The work presents an AI model to detect the COVID-19 by diagnoses of chest X-ray report. Chest X-ray report finding has been conducted using CNN (convolution neural network) model with ResNet50 and VGG 19 model. The model classify the patients into four category COVID-19, normal, pneumonia, lung obesity. AI model train the X-ray image through image processing methods with an accuracy of 99.3%. The efficacy of proposed model also has been analyzed in terms of accuracy, specificity, and sensitivity, precision. © 2022 IEEE.