Table 3.

Quantitative comparison of shallow and deep learning models. Accuracy (Acc.), dice similarity coefficient (DSC), jaccard index (JI), and time are reported for comparison. If the time is in second(s), then it is the inference time; otherwise, it is the model training time. SGD is the abbreviation for stochastic gradient descent, ReLu for rectified linear unit, lr for learning rate, and AF for activation function

Methods	Optimizer	AF	LR scheduling	Images size	Pre-processing step	Dataset	Technique	Acc.	DSC	JI	Time
SL [150]	–	–	–	440 $\times$ 440	Image resizing	JSRT	Gaussian kernel distance matrix, FCM	97.8	–	–	–
SL [78]	–	–	–	2048 $\times$ 2048, 4020 $\times$ 4892	No pre-processing is performed	JSRT, MC, CXR-14	FCM, Level set algorithm	–	97.6	95.6	25–30(s)
SL [109]	–	–	–	–	No pre-processing is performed	Private	Linear discriminant, kNN, Neural Network, gray level thresholding	76.0	–	–	–
SL [173]	–	–	–	1024 $\times$ 1024	Images are resized using the bilinear interpolation	Private	Markov random field classifier, Iterated conditional modes	94.8	–	–	–
DL [66]	SGD	–	lr = 0.1 and it decreased to 0.01 after training 70 epochs	256 $\times$ 256	Image resizing	JSRT; MC	Residual learning, atrous convolution layers, network wise training	–	98.0	96.1	–
DL [121]	Adam	ELU, Sigmoid	lr = 0.00001 with ${\beta }_1$= 0.9 and ${\beta }_2$= 0.999	128 $\times$ 128, 256 $\times$ 256	Image resizing	JSRT	UNet, ELU, Highly restrictive regularization	–	97.4	95.0	33.0(hr)
DL [99]	Adam	ReLu	lr = 0.001	256 $\times$256	Image resizing and data augmentation by affine transformations	JSRT	UNet, cross-validation	–	98.0	97.0	–
DL [155]	SGD	ReLu, Softmax	lr = 0.01	512 $\times$ 512, 128 $\times$ 128	Image resizing and scaling	MC	AlexNet, ResNet-18, Patch classification, Reconstruction of lungs	96.9	94.0	88.07	–
DL [81]	SGD	ReLu, Softmax	–	2048 $\times$ 2048	Histogram equalization and local contrast normalization is applied	JSRT	Modified SegNet	96.2	95.0	–	3.0(hr)
DL [143]	–	ReLu, Softmax	–	2048 $\times$ 2048	No pre-processing is performed	JSRT	SegNet	–	95.9	–	–
DL [111]	Adam	ReLu, Softmax	lr = 0.0001 with ${\beta }_1$= 0.9 and ${\beta }_2$= 0.999	224 $\times$ 224	Image resizing and data augmentation by flipping, rotating, and cropping	JSRT, MC	Modified SegNet (Lf-SegNet)	98.73	–	95.10	–
DL [164]	SGD	–	lr = 0.02 with poly learning rate policy	512 $\times$ 512	Image resizing and data augmentation by image to image translation	JSRT, MC, NIH	ResNet-101, dilated convolution, CCAM, MUNIT	–	97.6	–	–
DL [85]	SGD	ReLu, Sigmoid	lr = 0.01 with decrease in by factor 10 when validation accuracy is not improved	512 $\times$ 512	Image resizing	JSRT, MC, Shenzhen	ResNet-101, UNet, self-attention modules	–	97.2	–	1.4(s)