Covid-19 outbreak Prediction with the Base of Deep Learning Vgg16

Abstract : In this paper, modifications in neoteric architectures such as VGG16, VGG19, ResNet50, and InceptionV3 are proposed for the classification of COVID-19 using chest X-rays. The proposed architectures termed “COV-DLS” consist of two phases: heading model construction and classification. The heading model construction phase utilizes four modified deep learning architectures, namely Modified-VGG16, Modified-VGG19, Modified-ResNet50, and Modified-InceptionV3. An attempt is made to modify these neoteric architectures by incorporating the average pooling and dense layers. The dropout layer is also added to prevent the overfitting problem. Two dense layers with different activation functions are also added. Thereafter, the output of these modified models is applied during the classification phase, when COV-DLS are applied on a COVID-19 chest X-ray image data set. Classification accuracy of 98.61% is achieved by Modified-VGG16, 97.22% by Modified-VGG19, 95.13% by Modified-ResNet50, and 99.31% by Modified-InceptionV3. COV-DLS outperforms existing deep learning models in terms of accuracy and F1-score.
 Considering significant practical advantages of lung ultrasound (LUS) over other imaging techniques, but difficulties for doctors in pattern recognition Most of the review articles deal with ML algorithm (COV19NET) for the detection of COVID-19 on chest radiographs can potentially be an effective tool in triaging patients, particularly in resource-stretched health-care systems.
 • The dropout layer is also added to prevent the overfitting problem. • In transfer learning, all related information is collected, and this knowledge is “transferred” to solve various other problems • The dropout layer is used to prevent the overfitting problem. (e average pooling layer is used to smooth the image. • Transfer Learning. Transfer learning collects the knowledge gained during learning and applies it to another problem by transferring that knowledge.
 We propose vgg16 algorithm to predict covid19 based on frame-based model that correctly distinguishes COVID-19 LUS Images from healthy and bacterial pneumonia data with a sensitivity of 0.90 0.08 and a specificity of 0.96, 0.04 using CNN Algorithm. To investigate the utility of the proposed Vgg16 method which compare lung CT scan image with dataset and predict covid - 19.
 • Various tests such as RT-PCR and RAT are used to determine whether a person is infected; however, they are very costly and time-consuming. • The dropout layer is used to prevent the overfitting problem. (e average pooling layer is used to smooth the image. • For many years, DL has been widely used in various spheres of industry, such as natural language processing (NLP), video recognition, medical science, and entertainment. In the field of medical science, it has been very useful in predicting and diagnosing diseases such as tumors, pneumonia, and cancer. • The is technique is now being used to identify COVID-19 from X-ray images. (is is achieved by using convolutional neural networks (CNN) and transfer learning to optimize the pre trained models and enhance their performance in identifying COVID-19 from X-ray images. • Tirteen dif ferent CNN models were used to achieve 95.38% accuracy by using ResNet50 and SVM. Minaee et al. proposed a model prepared on 5,000 X-ray images (2,000 for training and 3,000 for testing) for the detection of COVID-19. • Transfer learning was used to predict COVID-19 patients with the help of ResNet18, ResNet50, SqueezeNet, and DenseNet-121, achieving a sensitivity rate of around 98% and a specificity rate of around 90%
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