Transfer Learning Based Method for COVID-19 Detection From Chest X-ray Images (original) (raw)
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On the Detection of COVID-19 from Chest X-Ray Images Using CNN-based Transfer Learning
Computers, Materials & Continua
Coronavirus disease (COVID-19) is an extremely infectious disease and possibly causes acute respiratory distress or in severe cases may lead to death. There has already been some research in dealing with coronavirus using machine learning algorithms, but few have presented a truly comprehensive view. In this research, we show how convolutional neural network (CNN) can be useful to detect COVID-19 using chest X-ray images. We leverage the CNN-based pre-trained models as feature extractors to substantiate transfer learning and add our own classifier in detecting COVID-19. In this regard, we evaluate performance of five different pre-trained models with fine-tuning the weights from some of the top layers. We also develop an ensemble model where the predictions from all chosen pre-trained models are combined to generate a single output. The models are evaluated through 5-fold cross validation using two publicly available data repositories containing healthy and infected (both COVID-19 and other pneumonia) chest X-ray images. We also leverage two different visualization techniques to observe how efficiently the models extract important features related to the detection of COVID-19 patients. The models show high degree of accuracy, precision, and sensitivity. We believe that the models will aid medical professionals with improved and faster patient screening and pave a way to further COVID-19 research.
International Journal For Research In Applied Science & Engineering Technology, 2020
The ongoing novel corona virus has spread all over the world and became a pandemic. This pandemic situation has led to a major crisis in healthcare systems and the global economy. As Covid-19 positive patient's increasing day by day, the crucial task is to detect and monitor disease efficiently and facilitate the results of Covid-19 positive patients to cure them as soon as possible. Currently used RT-PCR (Reverse transcription-polymerase chain reaction) testing method act as a goldmine for detecting Covid-19. But the total turnaround time required for Disease diagnosis is very large. This long turnaround time sometimes leads to patient deaths. To avoid that and detecting Covid-19 positive patients in a less time ,author proposed a method in this paper that uses Chest x-ray images for patient diagnosis and disease classification. Deep learning architecture called Convolutional neural network helps in diagnosis of patient. The tremendous success of the Convolutional neural network at image processing tasks in recent years extremely increased the use of electronic medical records and diagnostic imaging. To train and test the neural model the paper used a publicly available dataset that contains COVID-19, pneumonia, and normal patient Chest X-ray images. Also for experimental analysis a CovidNet20, Convolutional architecture was developed for disease classification along with transfer learning DenseNet121 pretrained model used for training and testing of the classification model. The proposed model able to differentiate COVID-19 and normal images as binary classification with 100% and 99% accuracy on DenseNet121 and CovidNet20 model. And, on multiclass classification with COVID-19, Normal and Pneumonia as classes Densenet121 gives 97% and CovidNet20 gives 98% accuracy.
Employing transfer learning techniques for COVID-19 detection using chest X-ray
International Journal of Advances in Applied Sciences (IJAAS), 2024
Coronavirus 2 (SARS-COV-2) is a global emergency that continues to terrify the globe at an alarming rate. Some nations are still combating the virus, attempting to discover infected individuals early on to prevent the infection from spreading. In terms of identifying the pattern in the pictures, radiological patterns have been shown to have greater accuracy, sensitivity, and specificity. Publicly available datasets are used for the implementation. The data is divided into three categories: COVID, normal, and pneumonia patients. Transfer learning is a type of deep learning that allows pre-trained models to be used and achieves high accuracy by detecting various anomalies in limited medical datasets. An image dataset of 1109 pictures was used in this work, and training was done using two distinct models, ResNet50 and InceptionV3, to distinguish the patient categories. For ResNet and InceptionV3, the proposed model has an accuracy of 97.29 and 98.20, respectively, with a sensitivity of 100% for InceptionV3 and a specificity of 99.41% for ResNet50. With a 98.20% accuracy, complete sensitivity, and high specificity, this study presents a deep learning model that gives diagnostics for multiclass classification and attempts to discriminate COVID-19 patients using chest X-ray photos. Other illnesses can also be detected using the proposed model.
SSRN Electronic Journal, 2020
Introduction: The early automatic diagnosis of the novel coronavirus (COVID-19) disease could be very helpful to reduce its spread around the world. In this study, we revisit the identification of COVID-19 from chest X-ray images using Deep Learning. Methods: We collect a relatively large COVID-19 dataset comparing with previous studies that contain 309 real COVID-19 chest x-ray images. We also prepare 2,000 chest x-ray images of pneumonia cases and 1,000 images of healthy chest cases. Deep Transfer Learning is used to detect abnormalities in our image dataset. We fine-tune three, pre-trained convolutional neural networks (CNNs) models on a training dataset: DenseNet 121, NASNetLarge, and NASNet-Mobile. Results: The evaluation of our models on a test dataset show that these models achieve an average sensitivity rate of approximately 99.45 % and an average specificity rate of approximately 99.5 %. Conclusion: A larger dataset of COVID-19 X-ray images could lead to more accurate and reliable identification of COVID-19 infections using Deep Transfer Learning. However, the clinical diagnosis of COVID-19 disease is always necessary.
SN Computer Science
The COVID-19 pandemic creates a significant impact on everyone's life. One of the fundamental movements to cope with this challenge is identifying the COVID-19-affected patients as early as possible. In this paper, we classified COVID-19, Pneumonia, and Healthy cases from the chest X-ray images by applying the transfer learning approach on the pre-trained VGG-19 architecture. We use MongoDB as a database to store the original image and corresponding category. The analysis is performed on a public dataset of 3797 X-ray images, among them COVID-19 affected (1184 images), Pneumonia affected (1294 images), and Healthy (1319 images) (https:// www. kaggle. com/ tawsi furra hman/ covid 19-radio graphy-datab ase/ versi on/3). This research gained an accuracy of 97.11%, average precision of 97%, and average Recall of 97% on the test dataset.
CNN Based Transfer Learning Framework For Classification Of COVID-19 Disease From Chest X-ray
2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), 2021
Today SARS-COVID-2 causes Novel Coronavirus diseases throughout in more than 150 countries all over the world. The quicker diagnosis is very crucial to reduce the outbreak of this diseases. The clinic al studies regarding this disease has shown that patients lungs are very much affected after the infection of coronavirus. Chest X-Ray, CT Scan are the most effective imaging approaches for identification of COVID 19 disease. Deep Learning approaches are one of the important approaches of machine learning that gives a critical analysis regarding for study of large amount of image datasets that can make some earlier impact of diseases. in recent years. To analyze the disease 1000 images are used for training and 150 images are used for testing the data from an online available standardized dataset of Kaggle. Here the images are taken as Covid and Non-Covid as the 2 class levels to classify the images using CNN. Here the activation function ReLU provides more than 90 percent of accuracy ...
Detection of Covid-19 on X-Ray Image of Human Chest Using CNN and Transfer Learning
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
At the end of 2019, a new disease called Coronavirus Disease (COVID-19) originated in Wuhan, China. This disease is caused by respiratory tract infections, ranging from the common cold to serious diseases such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). In Indonesia, there are tests to detect COVID-19, such as PCR and Rapid Test. This detector takes a long time and is less accurate in producing a diagnosis. This study aims to classify chest X-ray images using CNN and Transfer Learning methods to diagnose COVID-19. The proposed model has 4 scenarios: CNN Handcraft Model, Transfer Learning (VGG 16, VGG 19, and ResNet 50). This model is accompanied by data augmentation and data balancing techniques using undersampling techniques. The dataset used in this study is the “Covid-19 (COVID-19 and Normal) Radiographic Database” with 13,808 data divided into two classes, namely COVID-19 and Normal. Each model built will produce values for accuracy, ...
Elsevier, 2022
COVID-19 cases are putting pressure on healthcare systems all around the world. Due to the lack of available testing kits, it is impractical for screening every patient with a respiratory ailment using traditional methods (RT-PCR). In addition, the tests have a high turnaround time and low sensitivity. Detecting suspected COVID-19 infections from the chest X-ray might help isolate high-risk people before the RT-PCR test. Most healthcare systems already have X-ray equipment, and because most current X-ray systems have already been computerized, there is no need to transfer the samples. The use of a chest X-ray to prioritize the selection of patients for subsequent RT-PCR testing is the motivation of this work. Transfer learning (TL) with fine-tuning on deep convolutional neural network-based ResNet50 model has been proposed in this work to classify COVID-19 patients from the COVID-19 Radiography Database. Ten distinct pre-trained weights, trained on varieties of large-scale datasets using various approaches such as supervised learning, self-supervised learning, and others, have been utilized in this work. Our proposed 2021_ _ _1 model, pre-trained on the iNat2021 Mini dataset using the SwAV algorithm, outperforms the other ResNet50 TL models. For COVID instances in the two-class (Covid and Normal) classification, our work achieved 99.17% validation accuracy, 99.95% train accuracy, 99.31% precision, 99.03% sensitivity, and 99.17% F1-score. Some domain-adapted (_ ℎ − 14) and in-domain (ChexPert, ChestX-ray14) models looked promising in medical image classification by scoring significantly higher than other models.
Transfer learning with Resnet-50 for detecting COVID-19 in chest X-ray images
Indonesian Journal of Electrical Engineering and Computer Science, 2022
The novel coronavirus, also known as COVID-19, initially appeared in Wuhan, China, in December 2019 and has since spread around the world. The purpose of this paper is to use deep convolutional neural networks (DCCN) to improve the detection of COVID-19 from X-ray images. In this study, we create a DCNN based on a residual network (Resnet-50) that can identify COVID-19 from two other classes (pneumonia and normal) in chest X-ray images. DCNN was evaluated using two classification methods: binary (BC-1: COVID-19 vs. normal, BC-2: COVID-19 vs. pneumonia) and multi-class (pneumonia vs. normal vs. COVID-19). In all experiments, four fold cross-validation was used to train and test the model. This architecture's average accuracy is 99.9% for BC-1, 99.8% for BC-2, and 97.3% for multiclass cases. The experimental findings demonstrated that the suggested system detects COVID-19 with an average precision and sensitivity of 95% and 95.1% for multi-class classification, respectively. According to our findings, the proposed DCNN may help health professionals in confirming their first evaluation of COVID-19 patients.
Transfer Learning for Detection of COVID-19 Infection using Chest X-Ray Images
2021 5th International Conference on Computing Methodologies and Communication (ICCMC), 2021
Coronavirus is a contagious disease that affects individuals in a large scale. Coronavirus had a huge impact on the nation's economy and human lifestyle. The motivation behind this study was establishing a better diagnosis test for coronavirus infection. The RT-PCR test is used to diagnose the coronavirus frequently and returned a negative result for an infected individual. Furthermore, this test remains prohibitively expensive for most citizens, and not everyone could afford it due to financial hardship. An efficient imaging approach is de veloped for the evaluation of lung conditions, which has been done by examining the chest X-ray or chest CT of an infected person. Deep Learning is the well-suited sub domain of Artificial Intelligence [AI] technology, which offers helpful examination to consider more number of chest X-rays images that can basically have an effect on coronavirus screening. The goal of this research is to cluster the radiograph images present in the dataset into COVID-19, healthy and viral pneumonia by making use of the artificial neural networks. The training dataset was fine-tuned with eleven previously trained convolutional neural architectures. The assessment of the models on a test sample shows that AlexNet, DenseNet-121, GoogleNet and S queezenet1.1 as the top performing models.