(The Gist of Science Reporter) AnamNet




  • A new software tool that reveals the severity of lung infections in COVID-19 patients has been developed by researchers from the Departments of Computational and Data Science (CDS) and Instrumentation and Applied Physics at the Indian Institute of Science (IISc), in collaboration with colleagues from the Oslo University Hospital and the University of Agder in Norway.
  •  It has been described in a recent study published in the journal IEEE Transactions on Neural Networks and Learning Systems. 
  • 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 software tool developed by the IISc-led team, 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. Such a tool can provide automated assistance to doctors and therefore help in faster diagnosis and better management of COVID-19.
  • 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.
  • 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. 
  • The study also compared AnamNet’s 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.



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Courtesy: Science Reporter