Table 8.

Optimizing CNN hyperparameters using COVIDx. For each independent parameter, we trained several architectures on COVIDx 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	92.08	85.20	86.99
	20			93.35	87.56	90.10
	30			93.41	87.74	91.61
	40			94.24	89.25	91.99
	30	8		93.86	88.56	91.03
		16		94.30	89.38	92.00
		32	Label Smoothing	94.05	88.88	91.35
VGG-19	10	32	Cross Entropy	92.53	86.04	87.29
	20			93.98	88.77	91.57
	30			93.60	88.06	89.24
	40			93.29	87.46	88.72
	30	8		94.49	89.73	92.14
		16		94.93	90.56	92.79
		32	Label Smoothing	93.79	88.40	90.10
ResNet-18	10	32	Cross Entropy	93.10	87.08	88.43
	20			93.60	88.06	90.07
	30			93.29	87.48	90.05
	40			93.86	88.53	90.87
	30	8		94.17	89.11	91.17
		16		94.43	89.60	92.49
		32	Label Smoothing	94.30	89.35	91.58
ResNet-34	10	32	Cross Entropy	94.05	88.89	91.41
	20			94.62	89.97	93.32
	30			94.74	90.19	92.53
	40			94.43	89.63	93.38
	30	8		94.87	90.44	92.43
		16		95.31	91.28	94.50
		32	Label Smoothing	94.62	89.96	92.50
ResNet-50	10	32	Cross Entropy	94.93	90.55	92.62
	20			94.81	90.34	93.37
	30			95.12	90.92	93.72
	40			94.81	90.35	93.14
	30	8		93.03	87.01	91.99
		16		95.57	91.76	95.35
		32	Label Smoothing	95.12	90.91	93.36
EfficientNet-B0	10	32	Cross Entropy	95.69	91.99	94.52
	20			95.19	91.02	93.38
	30			95.69	92.01	95.48
	40			95.00	90.72	95.00
	30	8		94.68	90.16	93.25
		16		95.38	91.40	94.88
		32	Label Smoothing	95.82	92.24	96.16