Chatterjee, Soham2023-07-172023-07-172022-0763p.http://hdl.handle.net/10263/7397Dissertation under the supervision of Dr. Sushmita MitraThe last 2 years have been adversely affected by the COVID-19 pandemic. Doctors usually detect Covid from CT slices from features such as ground glass, consolidation and pleural effusion. These features usually have complex contours, irregular shapes and rough boundaries. With increasing number of cases the workload on the radiologists have increased by leaps and bounds to analyze the lung CT scans for tracking the disease progression in the patient. Moreover manual analysis of the CT scans is also prone to human error. So automated segmentation of infected lung CT slices can help the doctors to diagnose the disease faster. With the advent of deep learning, various approaches have been built to tackle this problem of automated biomedical image segmentation. One such architecture is the U-Net by Ronnenberger et al. [14]. Various other approaches have been proposed which are all variations of the U-Net to achieve better segmentation performance. However, the U-Net and its variations suffer from high model complexity, due to which they easily overfit on limited labelled dataset which is a serious issue in medical image domain. To cater this problem of data scarcity, research in “few shot segmentation” has gained significant importance in the recent years. In this work, we have developed a deep neural network model called Few Shot Conditioner Segmenter Covid (FSCS-cov), an architecture to tackle the problem of segmenting different COVID- 19 lesions from limited number of COVID-19 infected lung CT slices using few - shot learning paradigm.enDiagnosis using deep learningCOVID-19SegmentationComputed TomographyFew shot learningOther