Table 2.

Segmentation Performance of Deep Learning Models With and Without the AWTFE method on the Validation set

Network Architecture	Backbone Network	Precision (%)	Recall (%)	IoU (%)	Dice (%)
UNet	MobileNet	94.09	94.69	89.52	94.15
UNet + AWTFE		95.86	95.89	92.28	95.66
LinkNet		93.47	93.90	88.27	93.14
LinkNet + AWTFE		95.52	95.49	91.63	95.27
FPN		93.53	93.97	88.37	93.46
FPN + AWTFE		95.67	96.33	92.45	95.79
UNet	DenseNet169	94.72	94.92	90.28	94.63
UNet + AWTFE		96.47	96.57	93.45	96.39
LinkNet		94.43	94.60	89.74	94.31
LinkNet + AWTFE		96.08	96.44	92.99	96.13
FPN		94.20	94.78	89.68	94.28
FPN + AWTFE		96.39	96.49	93.26	96.31
UNet	ResNet50	94.86	95.12	90.57	94.77
UNet + AWTFE		96.64	96.49	93.49	96.41
LinkNet		94.35	94.68	89.71	94.28
LinkNet + AWTFE		95.99	96.63	93.04	96.14
FPN		94.61	94.79	90.06	94.47
FPN + AWTFE		96.36	95.99	92.79	95.98
UNet	VGG16	94.96	95.04	90.58	94.79
UNet + AWTFE		96.63	96.03	93.11	96.18
LinkNet		94.25	94.65	89.63	94.19
LinkNet + AWTFE		96.33	96.07	92.85	95.99
FPN		94.99	95.45	91.00	95.03
FPN + AWTFE		96.41	96.87	93.66	96.52

Bold fonts indicate the better performance across models.

AWTFE = adaptive wavelet tensor feature extraction; FPN = Feature Pyramid Network; IoU = Intersection Over Union; VGG16 = Visual Geometry Group 16.