Abstract:
The latest threat to global health is COVID-19. It has a tremendous diffusion
rate and to combat with this pandemic, large scale testing and diagnosis is
required. RT-PCR is the most accurate screening for validating COVID19 infection, but it is highly dependent on swab technique and needs time
and resources. Thus, we need to find an alternative way to predict COVID19. Many researchers already conclude that COVID-19 is very related to
Pneumonia and lungs feature of COVID is related to that of Pneumonia.
There is ongoing research to detect Pneumonia [13] from Chest CT scans.
Lung segmentation can help us to detect pulmonary abnormalities[10].
In this article first we try to segment lungs from chest CT scan and investigate the problems we face for COVID cases in deep learning architectures
for lung segmentation. We propose an classical image processing algorithm
to detect Lung from chest CT.
As already mentioned that CNN is a great architecture to classify images,
we are going to use a deep CNN model for lung classification.
Covid is a new disease and we have to move faster to detect it. Hence, we
are going to use transfer learning approach and use knowledge of pneumonia
detection to classify COVID-19.
In deep learning weight initialization for deep neural network is a major
factor and can lead us to very different performance. In this article we
are going to propose an weight initialization technique for transfer learning
that can use not only the information about the architecture but also the
information of the new class with respect to other known classes.
