Table 8.

Quantitative comparison of multi-class disease classification. Area under curve (AUC) is 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	LR Scheduling	Optimizer	AF	Image size	Pre-processing step	Dataset	Technique	AUC
DL [99]	lr = 0.0001 and a decay of 0.00001 over each update	Adam	Sigmoid	224 $\times$224	Images are resized and horizontally flipped	CXR-14	DenseNet-121, CAM, UNet architecture	81.5
DL [175]	–	SGD	–	1024 $\times$ 1024	Image are resized and intensity ranges are rescaled	CXR-14	ResNet-50, AlexNet, GoogLeNet, VGG-16, transfer learning	69.62
DL [16]	lr = 0.1	–	Sigmoid	512 $\times$ 512, 1024 $\times$1024	Image are downsampled using bilinear interpolation	CXR-14	ResNet, AM, CAM, knowledge preservation	–
DL [52]	lr = 0.001 and is reduced by factor of 10 when validation loss not improved	Adam	ReLu, Sigmoid	256 $\times$ 256, 512 $\times$ 512	Images are resized using the bilinear interpolation	CXR-14, PLCO	DenseNet, spatial knowledge, lung segmentation	88.3
DL [51]	lr = 0.01 and is divided by 10 after 20 epochs	SGD	Sigmoid	224 $\times$ 224, 256 $\times$ 256	Images are resized, cropped, and horizontally flipped. ImageNet mean is also subtracted from the image	CXR-14	ResNet-50, DenseNet-121, AG-CNN. AG-Mask Interface	87.1
DL [136]	lr = 0.0001	Adam	Softmax	224 $\times$ 224	Images are resized using the bilinear interpolation and are also horizontally flipped	CXR-14	ResNet-18, Transfer learning, CAM, Data augmentation	84.94
DL [126]	lr = 0.00005	SGD	Sigmoid	1024 $\times$ 1024	Images are resized using the bilinear interpolation and additional techniques such as rotation, zoom and shifting are performed for data augmentation	AMC, SNUBH	ResNet-50, fine-tuning, CAM, curriculum learning	98.3
DL [178]	lr = 0.0001	Adam	ReLu, Sigmoid	128 $\times$ 128	Images are normalized to enhance the contrast. Further, rotation, scaling and flipping is performed for reducing the overfitting	CXR-14	Inception-ResNet-v2, dilated ResNet, transfer Learning, cross-validation	90.0
DL [44]	lr = 0.0001 and it is decreased by 10 times after every 3 epochs	Adam	Softmax, Sigmoid	224 $\times$ 224	Images normalization is performed	CXR-14	ResNet-18, DenseNet-121, MSML	84.32
DL [140]	–	–	–	236 $\times$ 236	Images are resized, cropped, flipped and rotated	MURA, CheXpert, CXR-14	ResNet-50, DenseNet-121, one cycle training, transfer learning	80.0
DL [53]	lr = 0.001 and is reduced by factor of 10 when validation loss not improved	Adam	Sigmoid	1024 $\times$ 1024	Images in PLCO dataset are resized	CXR-14, PLCO	DenseNet-121, transfer learning	84.1
DL [185]	lr = 0.0001 and is reduced by factor of 10 when validation loss not improved	Adam	ReLu, Sigmoid	512 $\times$ 512	Image are resized, cropped, and horizontally flipped for augmentation	CXR-14	DenseNet-121, SE, multi-map layer, max–min pooling	83.0
DL [154]	–	Adam	ReLu, Sigmoid	224 $\times$ 224	Images are resized and data augmentation is performed	CXR-14	MobileNet-V2, transfer learning	80.99
DL [13]	lr = 0.01, 0.001 and is reduced by a factor of 2 when validation loss not improve	Adam	Sigmoid	256 $\times$ 256, 480 $\times$ 480	Image resizing and data augmentation techniques such as rotation and flipping	CXR-14	ResNet-38, ResNet-50, ResNet-101, MLP, transfer learning	82.2