SN	Symbol	Description of the Symbols
1	$L_{CE}$	Cross Entropy-loss
2	$L_{DSC}$	Dice Similarity Coefficient-loss
3	m	Model number used for segmentation in the total number of models M
4	n	Image scan number a total number of image N.
5	${\overline{\mathcal{A}}}_{ai}\left(m\right)$	Mean estimated lung area for all images using AI model ‘m’
6	${\mathcal{A}}_{ai}\left(m,n\right)$	Estimated Lung Area using AI model ‘m’ and image ‘n’
7	${\mathcal{A}}_{gt}\left(n\right)$	GT lung area for image ‘n’
8	${\overline{\mathcal{A}}}_{gt}$	Mean ground truth area for all images N in the database
9	$FoM\left(m\right)$	Figure-of-Merit for segmentation model ‘m’
10	JI	Mean Jaccard Index for a specific segmentation model
11	DSC	Dice Similarity Coefficient for a specific segmentation model
12	$N_p$	Sample size required computed using power analysis
13	MoE	Margin-of-Error
14	TP, TN	True Positive and True Negative
15	FP, FN	False Positive and False Negative
16	yi	GT label
17	ai	SoftMax classifier probability
18	Yp	Ground truth image
19	$\widehat{Y_p}$	Estimated image
20	P	Total number of pixels in an image in x,y-direction
21	z	Z-score from standard z-table
22	K5-r46	Cross-validation protocol with 40% training and 60% testing
Deep Learning Segmentation Architectures
23	SegNet	SDL model for lung segmentation with reduced learning parameters
24	VGG-SegNet	HDL model designed by fusion of VGG-19 and SegNet architecture
25	ResNet-SegNet	HDL model designed by fusion of ResNet-50 and SegNet architecture
Conventional Model
26	NIH	National Institute of Health segmentation model