Date of Publication :15th November 2024

Abstract: Coronavirus (COVID-19) has been widely transmitting around worldwide since December 2019 and this sudden outbreak has affected the normal life cycle and caused many casualties. Computed Tomography (CT) has an important role in diagnosing the disease in the field of healthcare and therefore CT imaging models are developed and deployed for efficient diagnosis. COVID-19 detection and segmentation models are developed using Deep learning and machine learning methods and are still in research. This paper addresses the insights obtained from several research articles focusing on various learning approaches, and their advantages, with the datasets used in the methods for segmentation and identification of COVID-19 CT data. The limitations of existing methods that need to be overcome with the aid of detection and segmentation procedures are discusses. To enhance the accuracy and efficiency, the models still have some clinical implications, complexity, limited data, need advanced algorithms, and so on which needs to be improved in further research. This study provides good insight for conducting more efficient identification and segmentation analysis of COVID-19 images.

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