Table 6.

Quantitative comparison of pneumonia and pneumothorax detection methods. Dice similarity coefficient (DSC), F1-score, and the 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	DSC	F1-Score	AUC
DL [135]	Adam	Sigmoid	lr = 0.001 that is decreased by factor of 10 when validation loss is not improved	224 $\times$ 224	Image normalization	CXR-14	DenseNet-121, transfer learning	–	43.5	–
DL [77]	SGD	–	lr = 0.00105	512 $\times$ 512	Random scaling, shift in coordinate space, brightness and contrast adjustment, blurring with Gaussian blur	RSNA	ResNet50, ResNet101, mask-RCNN, data augmentation	–	–	–
DL [133]	Gradient Descent	ReLu, Softmax	lr = 0.0003	224 $\times$ 224, 227 $\times$ 227	Image resize and augmentation	Kaggle [112]	AlexNet, ResNet-18, DenseNet-201, SqueezeNet, transfer learning, data augmentation, cross-validation	–	93.5	95.0
DL [43]	Adam	–	lr = 0.00001 with learning rate decrease factor of 0.2	512 $\times$ 512	Image resizing and data augmentation techniques such as scaling, shear and rotation	CXR-14	single-shot detector RetinaNet with Se-ResNext101, cross-validation	–	–	–
DL [37]	–	ReLu, Softmax	lr = 0.00001	227 $\times$ 227	Image resizing	CXR-14	VGG-19, CWT, DWT, GLCM, transfer learning, SVM-linear, SVM-RBF, KNN classifier, RF, DT	–	92.15	–
DL [158]	Adam	ReLu, Sigmoid	lr = 0.001 with ${\beta }_1$= 0.9 and ${\beta }_2$= 0.999	224 $\times$ 224	Image normalization, resizing, cropping and data augmentation	CheXpert	DenseNet-122, transfer learning	–	–	70.8
DL [176]	Adam	ReLu, Softmax	lr = 0.0005 with ${\beta }_1$= 0.9, ${\beta }_2$= 0.999	768 $\times$ 768, 1024 $\times$ 1024	Image normalization and data augmentation with random Gamma correction, random brightness and contrast change, CLAHE, motion blur, median blur, horizontal flip, random shift, random scale, and random rotation	SIIM-ACR${}^{\mathrm{a}}$, MC	UNet, SE-Resnext-101, EfficientNet-B3, transfer learning	88.0	–	–
DL [1]	Adam	ReLu, Sigmoid	lr = 0.001 which is relatively dropped per epoch using the cosine annealing learning rate technique	256 $\times$ 256, 512 $\times$ 512	Image resizing, normalization and data augmentation using horizontal flip, one of random contrast, random gamma, and random brightness, one of elastic transform, grid distortion, and optical distortion	SIIM-ACR${}^{\mathrm{b}}$	UNet, ResNet-34, transfer learning, stochastic weight averaging	83.56	–	–

${}^{\mathrm{a}}$https://www.kaggle.com/c/siim-acr-pneumothorax-segmentation/data

${}^{\mathrm{b}}$https://www.kaggle.com/c/siim-acr-pneumothorax-segmentation/data