Covid-19 outbreak Prediction with the Base of Deep Learning Vgg16
Abstract :
In recent months, coronavirus disease 2019
(COVID-19) has infected millions of people worldwide. In
addition to the clinical tests like reverse transcription-
polymerase chain reaction (RT-PCR), medical imaging
techniques such as computed tomography (CT) can be used as a
rapid technique to detect and evaluate patients infected by
COVID-19. Conventionally, CT-based COVID-19 classification
is done by a radiology expert. In this paper, we present a deep
learning-based Convolutional Neural Network (CNN) model we
developed for the classification of COVID-19 positive patients
from healthy subjects using chest CT. We used 10979 chest CT
images of 131 patients with COVID-19 and 150 healthy subjects
for training, validating, and testing of the proposed model.
Evaluation of the results showed the precision of 92%,
sensitivity of 90%, specificity of 91%, F1-Score of 0.91, and
accuracy of 90%. We have used the regions of infection
segmented by a radiologist to increase the generalization and
reliability of the results. The plotted heatmaps show that the
developed model has focused only on the infected regions of the
lungs by COVID-19 to make decisions.
In recent months, coronavirus disease 2019
(COVID-19) has infected millions of people worldwide. In
addition to the clinical tests like reverse transcription-
polymerase chain reaction (RT-PCR), medical imaging
techniques such as computed tomography (CT) can be used as a
rapid technique to detect and evaluate patients infected by
COVID-19. Conventionally, CT-based COVID-19 classification
is done by a radiology expert. In this paper, we present a deep
learning-based Convolutional Neural Network (CNN) model we
developed for the classification of COVID-19 positive patients
from healthy subjects using chest CT. We used 10979 chest CT
images of 131 patients with COVID-19 and 150 healthy subjects
for training, validating, and testing of the proposed model.
Evaluation of the results showed the precision of 92%,
sensitivity of 90%, specificity of 91%, F1-Score of 0.91, and
accuracy of 90%. We have used the regions of infection
segmented by a radiologist to increase the generalization and
reliability of the results. The plotted heatmaps show that the
developed model has focused only on the infected regions of the
lungs by COVID-19 to make decisions.
In recent months, coronavirus disease 2019
(COVID-19) has infected millions of people worldwide. In
addition to the clinical tests like reverse transcription-
polymerase chain reaction (RT-PCR), medical imaging
techniques such as computed tomography (CT) can be used as a
rapid technique to detect and evaluate patients infected by
COVID-19. Conventionally, CT-based COVID-19 classification
is done by a radiology expert. In this paper, we present a deep
learning-based Convolutional Neural Network (CNN) model we
developed for the classification of COVID-19 positive patients
from healthy subjects using chest CT. We used 10979 chest CT
images of 131 patients with COVID-19 and 150 healthy subjects
for training, validating, and testing of the proposed model.
Evaluation of the results showed the precision of 92%,
sensitivity of 90%, specificity of 91%, F1-Score of 0.91, and
accuracy of 90%. We have used the regions of infection
segmented by a radiologist to increase the generalization and
reliability of the results. The plotted heatmaps show that the
developed model has focused only on the infected regions of the
lungs by COVID-19 to make decisions.
In recent months, coronavirus disease 2019
(COVID-19) has infected millions of people worldwide. In
addition to the clinical tests like reverse transcription-
polymerase chain reaction (RT-PCR), medical imaging
techniques such as computed tomography (CT) can be used as a
rapid technique to detect and evaluate patients infected by
COVID-19. Conventionally, CT-based COVID-19 classification
is done by a radiology expert. In this paper, we present a deep
learning-based Convolutional Neural Network (CNN) model we
developed for the classification of COVID-19 positive patients
from healthy subjects using chest CT. We used 10979 chest CT
images of 131 patients with COVID-19 and 150 healthy subjects
for training, validating, and testing of the proposed model.
Evaluation of the results showed the precision of 92%,
sensitivity of 90%, specificity of 91%, F1-Score of 0.91, and
accuracy of 90%. We have used the regions of infection
segmented by a radiologist to increase the generalization and
reliability of the results. The plotted heatmaps show that the
developed model has focused only on the infected regions of the
lungs by COVID-19 to make decisions.
In recent months, coronavirus disease 2019
(COVID-19) has infected millions of people worldwide. In
addition to the clinical tests like reverse transcription-
polymerase chain reaction (RT-PCR), medical imaging
techniques such as computed tomography (CT) can be used as a
rapid technique to detect and evaluate patients infected by
COVID-19. Conventionally, CT-based COVID-19 classification
is done by a radiology expert. In this paper, we present a deep
learning-based Convolutional Neural Network (CNN) model we
developed for the classification of COVID-19 positive patients
from healthy subjects using chest CT. We used 10979 chest CT
images of 131 patients with COVID-19 and 150 healthy subjects
for training, validating, and testing of the proposed model.
Evaluation of the results showed the precision of 92%,
sensitivity of 90%, specificity of 91%, F1-Score of 0.91, and
accuracy of 90%. We have used the regions of infection
segmented by a radiologist to increase the generalization and
reliability of the results. The plotted heatmaps show that the
developed model has focused only on the infected regions of the
lungs by COVID-19 to make decisions.
In recent months, coronavirus disease 2019
(COVID-19) has infected millions of people worldwide. In
addition to the clinical tests like reverse transcription-
polymerase chain reaction (RT-PCR), medical imaging
techniques such as computed tomography (CT) can be used as a
rapid technique to detect and evaluate patients infected by
COVID-19. Conventionally, CT-based COVID-19 classification
is done by a radiology expert. In this paper, we present a deep
learning-based Convolutional Neural Network (CNN) model we
developed for the classification of COVID-19 positive patients
from healthy subjects using chest CT. We used 10979 chest CT
images of 131 patients with COVID-19 and 150 healthy subjects
for training, validating, and testing of the proposed model.
Evaluation of the results showed the precision of 92%,
sensitivity of 90%, specificity of 91%, F1-Score of 0.91, and
accuracy of 90%. We have used the regions of infection
segmented by a radiologist to increase the generalization and
reliability of the results. The plotted heatmaps show that the
developed model has focused only on the infected regions of the
lungs by COVID-19 to make decisions.
Coronavirus disease (COVID-19) is a viral pneumonia that originated in China and has rapidly spread around the world. Early diagnosis is important to provide effective and timely treatment. Thus, many studies have attempted to solve the COVID-19 classification problems of workload classification, disease detection, and differentiation from other types of pneumonia and healthy lungs using different radiological imaging modalities. To date, several researchers have investigated the problem of using deep learning methods to detect COVID-19, but there are still unsolved challenges in this field, which this review aims to identify. The existing research on the COVID-19 classification problem suffers from limitations due to the use of the binary or flat multiclass classification, and building classifiers based on only a few classes. Moreover, most prior studies have focused on a single feature modality and evaluated their systems using a small public dataset. These studies also show a reliance on diagnostic processes based on CT as the main imaging modality, ignoring chest X-rays, as explained below. Accordingly, the aim of this review is to examine existing methods and frameworks in the literature that have been used to detect and classify COVID-19, as well as to identify research gaps and highlight the limitations from a critical perspective. The paper concludes with a list of recommendations, which are expected to assist future researchers in improving the diagnostic process for COVID-19 in particular. This should help to develop effective radiological diagnostic data for clinical applications and to open future directions in this area in general.
Keywords: artificial intelligence; COVID-19; CXR; CT-scan; deep learning; diagnosis; image classification; multi-classes; pneumonia
• COV-DLS outperforms existing deep learning models in terms of accuracy and F1-score.
• It is observed in the extant literature that the existing models are able to identify COVID-19 from chest X-ray images
• Their proposed technique achieved better results than the existing classical and deep learning techniques, and it can be further enhanced by using the segment of ground-glass opacity.
• The developed technique was tested on three different data sets and achieved better performance measures than the existing deep learning ar chitectures.
• 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.
• To solve a complex problem, some architectural layers can be appended to increase performance and accuracy. In general, the number of layers is increased to reduce the error rate, but at a certain point, a common problem, known as the “vanishing/exploding gradient,” occurs. ResNet architecture overcomes this problem by introducing skip connections or identity shortcut connection techniques.
• The proposed architectures termed “COV-DLS” consist of two phases: heading model construction and classification. (e heading model construction phase utilizes four modified deep learning architectures, namely Modified-VGG16, Modified-VGG19, Modified-ResNet50, and Modified-InceptionV3.
• In this paper, novel deep transfer learning techniques termed “COV-DLS” are proposed for discriminating coronavirus infection in chest X-ray images.
• The sensitivity for the proposed model was 96%, and specificity was 70.65%. Makris et al. [5] developed a DL model to identify COVID-19 patients from chest X-rays; convolutional neural networks (CNN) were utilized in this model.
• 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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