COVID-19 Semantic Pneumonia Segmentation and Classification Using Artificial Intelligence

Table 1

Summary of the related works.

Ref.	A	Results (%)	Limitations
[15]	Truncated inception network	98.5	(i) Limited dataset is used.
			(ii) In stacking, the original dimensions are solved.
			(iii) Images and the structured images must be the same.
[16]	DarkCovidNet	87.02	(i) End-to-end architecture.
			(ii) Manual feature extraction.
			(iii) Including a severely low number of image samples.
			(iv) Imprecise localization on the chest region
[9]	Bayes-SqueezeNet	97.9	(i) This study is conducted on a publicly dataset, which contains less than 100 COVID-19 images, and more than 5,000 non-COVID images. Due to the limited number of COVID-19 images publicly available so far, further experiments are needed on a larger set of cleanly labeled COVID-19 images for a more reliable estimation of the sensitivity rates.
[17]	DenseNet	85	(ii) Limited dataset is used.
[18]	MobileNet	94.7	—
[19]	Resnet-50+SVM	94.7	(i) The limitation of this methodology is that if the patient is in a critical situation and unable to attend for Xray scanning.
			(ii) Small dataset.
			(iii) Authors involved SARS&MER cases in COVID positive classes.
[20]	CXRVN	97.5	(i) Time consuming.
			(ii) Lack of extract semantic reliable features.
			(iii) Binary classifier.