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IISC-Bengaluru researchers develop software tool to diagnose Covid-19 severity in lungs

IISC-Bengaluru researchers develop software tool to diagnose Covid-19 severity in lungs February 19, 2021 × Researchers at the Indian Institute of Science (IISc) designed a special tool called ‘AnamNet’ to diagnose the severity of coronavirus infection in patients’ lungs. The study was carried out by researchers from the Departments of Computational and Data Science (CDS) and Instrumentation and Applied Physics at IISc. They conducted the study in collaboration with colleagues from the Oslo University Hospital and the University of Agder in Norway. The study was published in the journal IEEE Transactions on Neural Networks and Learning Systems. To develop this software tool, the researchers employed a unique neural network and deep learning, and other image processing techniques. Their tool finds specific abnormal features and estimates the damage in the lungs and identifies infected areas in a chest CT scan with a high level of accuracy.

AI-based software tool to aid automated COVID-19 lung infection diagnosis

Updated Feb 18, 2021 | 13:27 IST 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. AI-based software tool to aid automated COVID-19 lung infection diagnosis | Photo Credit: Pixabay  Bengaluru: A new software tool that reveals 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 the 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

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