Table 5.

Quantitative comparison of TB detection methods. Accuracy, sensitivity, and area under curve (AUC) are reported for comparison. SGD is the abbreviation for stochastic gradient descent, ReLu for rectified linear unit, lr for learning rate, and AF for activation function

Method	Optimizer	AF	LR scheduling	Images size	Pre-processing step	Dataset	Technique	Accuracy	Sensitivity	AUC
CAD [168]	–	–	–	932 $\times$ 932	Lung field segmentation using ASM	Private	ASM, Gaussian derivative filter, kNN algorithm	–	86.0	82.0
CAD [74]	–	–	–	2048 $\times$ 2048, 1023 $\times$ 1005	Lung field segmentation using intensity, lung model and Log Gabor masks	JSRT, MC	lung model, intensity and LoG mask, SVM, leave-one out evaluation	75.0	–	83.12
CAD [75]	–	–	–	2048 $\times$ 2048	Lung field segmentation using graph cut method	JSRT, MC, Shenzhen	Graph cut segmentation, ODI, CBIR, SVM	84.0	–	90.0
DL [65]	SGD	ReLu, Softmax	lr = 0.01 and it is decreased by a factor of 2 for every 30 epochs	520 $\times$ 520	Images resizing and data augmentation	MC, Shenzhen	AlexNet, transfer learning, dataset augmentation, threefold cross-validation	90.3	–	96.4
DL [88]	SGD	–	Training from scratch, lr = 0.01, pre-trained network, lr = 0.001	256 $\times$ 256	Data augmentation and histogram equalization	MC, Shenzhen	GoogLeNet, AlexNet, transfer learning, data augmentation	–	–	99.0
DL [59]	Adam	ReLu, Softmax	lr = 0.0001	224 $\times$ 224	No pre-processing is performed	MC, Shenzhen	CNN	94.73	–	–
DL [49]	Nesterov ADAM	ReLu	lr = 0.001 and decreased by factor of 10 when validation loss stops improving	224 $\times$ 224	Image resizing, normalization, and data augmentation	MC, Shenzhen, CXR-14	DenseNet121, transfer learning, meta data	–	–	93.7
DL [128]	Adam	ReLu, Softmax	lr = 0.001	512 $\times$ 512	Image cropping, resizing, and normalization	MC, Shenzhen	CNN, grad-CAM, cross-validation	86.2	–	92.5
DL [134]	SGD	ReLu, Softmax	lr = 0.001	224 $\times$ 224	Image normalization using Z-score, lung segmentation and data augmentation	MC, Shenzhen	ResNet18, ResNet-101, VGG-19, InceptionV3, UNet, transfer learning, score-CAM	98.6	98.56	–
DL [114]	SGD	ReLu	lr = 0.01 with rate decay of 0.5	224 $\times$ 224	Unsharp Masking, High-Frequency Emphasis Filtering, and Contrast Limited Adaptive Histogram Equalization, cropping, image normalization	Shenzhen	ResNet-18, ResNet-50, EfficientNet-B4, UM, HEF, CLAHE, transfer learning	89.92	–	94.8
DL [11]	–	ReLu	–	300 $\times$ 300, 229 $\times$ 229	Image normalization and resizing	MC, Shenzhen	Inception-v3, MobileNet, ResNet50, Gabor filter, cross-validation	97.59	–	99.0
DL [39]	–	ReLu, Softmax	–	224 $\times$ 224, 320 $\times$ 320	Image resizing and data augmentation	MC, Shenzhen, COVID-19	Transformer model, EfficientNetB0, EfficientNetB1, transfer learning	97.72	–	100
DL [119]	–	–	–	512 $\times$ 512	Image resizing, normalization, and data augmentation	MC, Shenzhen, Private	ResNext, UNet	91.0	85.7	91.0