vioft2nntf2t|tblJournal|Abstract_paper|0xf4ffb5c932000000c0a8130001000100 Automatic diagnosis of Covid-19 lung complications from Computerized Tomography (CT) scans is an increasingly important research topic. In this rapidly developing area of Covid detection from medical image sequences, it is noted that most prior literature has focused on binary classification to detect diseased versus healthy cases from single X-ray or CT image. In this paper, we advance a step further by presenting a comprehensive framework for automated classification of the severity of lung infection (mild, moderate and severe) from CT sequences of confirmed Covid cases. We consider the sequence information for automation because in practice, the medical experts look at the CT sequence to score the severity of infection. We have collected a new lung CT sequence dataset at various stages of Covid infection from Indian patients. This dataset has been scored in terms of the severity of each lung lobe by experts in the field. We present a novel application of space-time transformers for CT sequences and achieve 93.3% accuracy for sequence level and 99% accuracy for patient-level, for multi- class classification of severity classes.
Mercy Ranjit1, Gopinath Ganapathy2, K. Vishnu Vardhan Reddy3, Sahana M. Prabhu4
Bharathidasan University, India1,2, Navodaya Medical College Hospital and Research Centre, India3, Robert Bosch Engineering and Business Solutions, Bengaluru, India4
Artificial Intelligence, Computerized Tomography, Covid-19, Deep Learning, Image Classification, Vision Transformer
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| Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 13 , Issue: 4 , Pages: 3028 - 3034 )
Date of Publication :
May 2023
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339
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