Skin Lesion Image Segmentation Algorithm Based on Multi-Scale DenseNet

Fig. 7. Image pre-processing. (a) Original lesion image; (b) outcome of closing morphological operation for artifacts removal; (c) resultant smooth image; (d) sharp image after convolution of un-sharped filter

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Fig. 8. Segmentation results using different algorithms. (a) Input images; (b) labels; (c) results by MSDN; (d) results by U-Net; (e) results by FCN

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Table 1. Results of comparative experiments

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Table 1. Results of comparative experiments

(α, β, γ)	A_cc /%	D_ic /%	J_ac /%	S_en /%	S_pe /%
(0.6, 0.4, 0.3)	94.19	93.21	89.30	88.08	96.12
(0.7, 0.3, 0.7)	94.31	93.93	89.26	89.53	96.32
(0.8, 0.2, 0.5)	93.87	92.68	89.16	90.23	95.44
(0.9, 0.1, 0.6)	93.07	92.03	88.69	88.12	95.63
(0.7, 0.3, 0.5)	95.48	96.37	93.41	92.93	96.49

Table 2. Performance evaluation of MSDN in ISBI 2016 dataset under different conditions
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Table 2. Performance evaluation of MSDN in ISBI 2016 dataset under different conditions
Experiment A_cc /% D_ic /% J_ac /% S_en /% S_pe /%
MSDN-PPB 94.50 94.30 92.11 92.66 95.50
MSDN-L_Total 95.32 92.90 89.13 89.00 96.48
MSDN 95.48 96.37 93.41 92.93 96.49

Table 3. Performance comparison of segmentation results in ISIB 2016 dataset

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Table 3. Performance comparison of segmentation results in ISIB 2016 dataset

Method	A_cc /%	D_ic /%	J_ac /%	S_en /%	S_pe /%
EXB	95.30	91.00	84.30	91.00	96.50
CUMED	94.90	89.70	82.90	91.10	95.70
Mahudur	95.20	89.50	82.20	88.00	96.90
SFU-mial	94.40	88.50	81.10	91.50	95.50
TMUteam	94.60	88.80	81.00	83.20	98.70
FCN	94.13	88.64	81.37	91.70	94.90
MFCN^[14]	95.51	91.18	84.64	92.17	96.54
J-FCN^[15]	95.50	91.20	84.70	91.80	96.60
SSLS^[16]	84.67	69.97	57.20	70.04	97.31
MSDN	95.48	96.37	93.41	92.93	96.49

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Guoliang Yang, Zhendong Lai, Yang Wang. Skin Lesion Image Segmentation Algorithm Based on Multi-Scale DenseNet[J]. Laser & Optoelectronics Progress, 2020, 57(18): 181020

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