[Retracted] An Efficient Stacked Deep Transfer Learning Model for Automated Diagnosis of Lyme Disease

Table 2

Training analyses among the proposed and competitive Lyme rash classification models.

Model	TP	FP	TN	FN	Accuracy	f-measure	Sensitivity	Specificity	AUC
CNN [10]	196	20	294	26	90.4306	91.5032	87.9069	93.3333	91.0679
EDLP [9]	192	24	303	17	89.6551	94.3708	92.4444	92.2330	92.3221
SqueezeNet [11]	194	22	294	26	89.3719	92.1921	87.6777	93.3130	91.1111
LWADL [12]	210	6	295	25	97.0731	92.4471	88.8392	98.0769	94.2164
Unet-dCNN [13]	184	32	297	23	86.0262	92.6282	89.5454	90.0311	89.8336
ResNet-50 [15]	202	14	288	32	93.1372	89.8089	85.5855	95.2702	91.1196
FADEM [16]	195	21	292	28	90.1408	91.3580	87.2727	93.3753	90.8752
Ensemble CNN [17]	195	21	310	10	90.7894	96.633	95.3917	93.1818	94.0952
Proposed without EBCC	197	19	316	4	90.5940	98.6486	97.8609	93.8906	95.3815
Proposed with EBCC	213	3	316	4	98.6111	98.6711	98.1566	99.0000	98.6460