Melanoma segmentation based on deep learning

Figures & data

Figure 1. The abscissa of the graph is the training step of the network model, and the ordinate is the accuracy. There are two sets of data sets: the test set and the training set. It can be seen from the figure that the accuracy of the training set increases as the number of training steps increases; however, the test set is stopped at an early stage due to the small amount of data, and then the training is over, the accuracy of training is reduced.

Figure 2. The graph shows the contrast of the three activation functions.

Figure 3. Use multi-layer filters for feature learning.

Figure 4. The ‘conv3*3’ in this diagram represents the convolution calculation using the 3*3 filter, and the ‘up’ indicates that the upper layer uses a 2*2 filter and the ‘drop’ represents the dropout layer.

Table 1. The table is the architecture of the improved convolution neural network, including the names of the layers, the functions used and the corresponding parameters.

Name	function	variant	Name	function	variant
Convolution1	Inception	32	Convolution6	Inception	256
Pool1	Nconvolution	32*3*3	Convolution6	Dropout	0.5
Pool1	Dropout	0.5	After_conv3	Rblock	1,128
Convolution2	Inception	64	Up7	Merge	2*2
Pool2	Nconvolution	64*3*3	Convolution7	Inception	128
Pool2	Dropout	0.5	Convolution7	Dropout	0.5
Convolution3	Inception	128	After_conv2	Rblock	1,64
Pool3	Nconvolution	128*3*3	Up8	Merge	2*2
Pool3	Dropout	0.5	Convolution8	dropout	0.5
Convolution4	Inception	256	After_conv1	Rblock	1,32
Pool4	Nconvolution	256*3*3	Up9	Merge	2*2
Pool4	Dropout	0.5	Convolution9	Inception	32
Convolution5	Inception	512	Convolution9	Dropout	0.5
Convolution5	Dropout	0.5	Convolution10	Convolution	1*1*1
After_conv4	Rblock	1,256	output	Flatten, Densen	sigmoid
Up6	Merge	2*2

Table 2. The table is the structure of the function Inception and Nconvolution.

Inception:	Convolution	ELU	Convolution	BN	ELU	MaxPooling	Convolution	Merge
Nconvolution:	Convolution	BN	ELU

Figure 5. The figure is the result of the melanoma test set segmentation. The first picture is segmented by a clear red line, and the second is the binarized mask of the first graph.

Figure 6. The graph contains 20 melanoma image segmentation results. The first and fourth rows are the result of the segmentation of the 20 melanoma images using the improved CNN, the lesion area is circled with the red line; the second and fifth rows are the mask of the test image segmentation; the third and sixth rows are Experts’ manual segmentation, ie ground truth mask. The results of the test segmentation are compared with the ground truth.

Figure 7. The figure is the indicator of the melanoma test segmentation (FP, TN, TP, FN), by comparing the test segmentation results with the expert segmentation results.

Table 3. The table is the indicators of the melanoma image test set segmentation results TPR, FPR, TNR, FNR, Precision, Recall, Ffactor, RandIndex, ARI, where the seven image indicators correspond to the above seven mask images.

ImageNo Value Measurement	1	2	3	4	5	6	7
True Positive Rate (TPR)= TP/(TP + FN) =Sensitivity	0.886688	0.911996	0.94458	0.962961	0.965972	0.965972	0.998674
False Positive Rate (FPR)= FP/(FP + TN)	0.000933	0	0.000110928	0.001526	0.005927	0.00592723	0.024422
True Negative Rate (TNR)= TN/(FP + TN) =Specificity	0.999067	1	0.999889	0.998474	0.994073	0.994073	0.975578
False Negative Rate (FNR)= FN/(TP + FN)	0.113312	0.088004	0.055420299	0.037039	0.034028	0.034028027	0.001326
Precision = TP/(TP + FP)	0.978084	1	0.997704963	0.989365	0.983625	0.983624606	0.883393
Recall = TP/(TP + FN)	0.886688	0.911996	0.944579701	0.962961	0.965972	0.965971973	0.998674
Ffactor =2 × (precision × recall)/(precision + recall)	0.930146	0.953973	0.970415793	0.975985	0.974718	0.974718372	0.937503
RandIndex (RI)	0.988121	0.952611	0.994420755	0.987895	0.973374	0.973373948	0.959243
AdjustedRandIndex (ARI)	0.921	0.900282	0.965969847	0.964892	0.944108	0.944108097	0.898418

Table 4. Lesion Segmentation Result. Higher results is better.

	TPR	TNR	AC = (TP + TN)/(TP + FP + FN + TN)
Our	0.95	0.94	0.91
SegNet [20]	0.87	0.96	0.98
FCN [21]	0.85	0.70	0.82

Badrinarayanan V, Handa A, Cipolla R. A deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling, arXiv preprint arXiv:1505.07293, 2015.

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Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation, IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440, 2015.

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