Table 1.

Summary of related works

Reference	Motivation	Dataset size	Limitations/research gap
[5]	Finding COVID-19 symptoms progress in pregnant women’s is quite challenge	59	Very small dataset, majority images are acquired from women which includes pregnant women and children’s, acquired images are low resolution
[10]	To generate more number of COVID-19 images	624	Lack of clinical studies and the generated images are low resolution
[11]	To generate more number of COVID-19 images sing GAN	306	Lack of testing and validated data
[12]	To increase the COVID-19 CT images with available limited CT images	742	Augmented images are low resolution and lack of clinical studies
[13]	To differentiate the symptoms of COVID-19 from general lung disease	50	Very small dataset and differentiating the sign of COVID-19 is difficult from the lung diseases
[15]	To analyze the characteristics of COVID-19 diseases using CT images	37	Very small dataset
[16]	To find the infected region of COVID-19 using CT images	4	Very small dataset
[17]	To study the CT images temporal changes in COVID-19-infected persons	90	Very small dataset
[18]	The RT-PCR testing costly and limited in numbers	757	Limited training data and classification accuracy is poor
[19]	The RT-PCR testing is the time consuming process	618	Training and testing samples are limited
[20]	High false negative in RT-PCR testing	742	Acquired CT images are showing more number of artifacts
[21]	To monitor the periodical changes of COVID-19-infected lungs	126	Very small dataset, lack of architecture information, and systematic evaluation has not presented
[24]	To develop an automated toolkit for COVID-19 detection instead of manual detection	361	Limited amount of training and testing images
[25]	To effectively use DL to make shortage of medical professional in this pandemic situation	646	Limited training data
[27]	Radiographic patterns of Ct slices produced better performance than RT-PCR test	618	Lack of clinical studies