Table 7.

Optimizing CNN hyperparameters using COVIDcxr. For each independent parameter, we trained several architectures on COVIDcxr to examine the effects of various hyperparameters on the accuracy of COVID-19 CXR classification.

CNN	Epoch	Batch Size	Loss Function	Acc (%)	MCC (%)	F1 (%)
VGG-16	10	32	Cross Entropy	77.08	68.15	76.26
	20			77.60	66.71	77.43
	30			80.73	72.29	80.53
	40			83.33	75.51	83.31
	30	8		85.42	79.20	85.43
		16		84.38	76.61	84.27
		32	Label Smoothing	79.17	69.62	78.91
VGG-19	10	32	Cross Entropy	78.65	68.38	78.89
	20			82.81	74.25	82.96
	30			84.90	77.74	84.92
	40			84.38	76.66	84.35
	30	8		84.90	78.36	84.96
		16		82.81	74.90	82.74
		32	Label Smoothing	85.42	78.30	85.51
ResNet-18	10	32	Cross Entropy	81.25	73.69	81.25
	20			82.29	74.21	82.45
	30			85.94	79.40	86.14
	40			85.42	78.16	85.37
	30	8		81.25	73.56	81.39
		16		82.29	74.20	82.37
		32	Label Smoothing	84.38	76.95	84.46
ResNet-34	10	32	Cross Entropy	81.25	72.10	81.20
	20			81.25	71.93	80.91
	30			79.69	70.02	79.70
	40			81.25	71.94	81.23
	30	8		86.46	80.12	86.54
		16		85.94	79.00	85.87
		32	Label Smoothing	83.85	76.08	83.85
ResNet-50	10	32	Cross Entropy	81.77	73.18	82.09
	20			84.90	77.32	84.93
	30			82.81	75.31	83.12
	40			85.42	78.12	85.45
	30	8		86.46	80.49	86.52
		16		86.98	80.84	87.16
		32	Label Smoothing	83.85	76.21	84.05
EfficientNet-B0	10	32	Cross Entropy	83.33	75.36	83.65
	20			84.38	76.67	84.41
	30			88.02	82.01	88.00
	40			85.42	78.10	85.42
	30	8		88.02	82.06	87.89
		16		86.98	80.45	86.99
		32	Label Smoothing	88.54	82.83	88.62