the severity of lung infections in COVID-19 patients has been
developed by researchers from the Indian Institute of Science,
in collaboration with Oslo University Hospital and University
of Adger in Norway.
It has been described in a recent study published in the
journal "IEEE Transactions on Neural Networks and Learning
Systems", Bengaluru-based IISc said in a statement.
COVID-19 can cause severe damage to the respiratory
systems, especially the lung tissues. Image-based methods such
as X-ray or CT scans can prove helpful in determining how bad
the infection is, the statement noted.
The software tool, developed by the Departments of
Computational and Data Science and Instrumentation and Applied
Physics at IISc, called AnamNet, can ''read'' the chest CT scans
of COVID-19 patients, and, using a special kind of neural
network, estimate how much damage has been caused in the
lungs, by searching for specific abnormal features, it said.
"Such a tool can provide automated assistance to doctors
and therefore help in faster diagnosis and better management
of COVID-19", according to IISc.
AnamNet employs deep learning and other image processing
techniques, which have now become integral to biomedical
research and applications. The software can identify infected
areas in a chest CT scan with a high degree of accuracy, it
The researchers trained AnamNet to look for abnormalities
and classify areas of the lung scan as either infected or not
infected - this is called segmentation.
The tool can judge the severity of the disease by
comparing the extent of infected area with healthy area.
"It basically extracts features from the chest CT images
and projects them onto a non-linear space (a mathematical
representation), and then recreates the (segmented) image from
this representation. This is called anamorphic image
processing," explains Naveen Paluru, first author and PhD
student in the lab of Phaneendra Yalavarthy, Associate
Professor at CDS.
The study also compared AnamNets performance with other
state-of-the-art software tools which perform similar tasks.
It not only matched its peers in its accuracy, but also
performed just as well using fewer parameters. The neural
network was also computationally less complex, which allowed
the researchers to train it much faster to detect anomalies,
it was stated.
Another significant advantage of AnamNet is that the
software is lightweight with a small memory footprint. This
has enabled the team to develop an app called CovSeg that can
be run on a mobile phone and hence potentially be used by
"We felt the need for a lightweight framework that could
be deployed as a point-of-care diagnostic device on
smartphones or a Raspberry Pi," says Paluru.
He adds that this feature is missing from currently
available state-of-the-art technologies such as UNet, which
requires specialised hardware.
According to the authors, AnamNet holds promise beyond
merely identifying lung infections in COVID-19 patients.
"We are currently focusing on making our software more
robust to handle COVID-19 scans, but we are also looking to
diversify to other common lung diseases like pneumonia,
fibrosis and even lung cancer in the near future, Yalavarthy
He suggests that with some changes to the present design,
the software could even be used to read brain scans.
The software tool is freely available to the public, IISc
added. PTI RS APR