Skin Lesion Segmentation with Improved Convolutional Neural Network

Abstract

Recently, the incidence of skin cancer has increased considerably and is seriously threatening human health. Automatic detection of this disease, where early detection is critical to human life, is quite challenging. Factors such as undesirable residues (hair, ruler markers), indistinct boundaries, variable contrast, shape differences, and color differences in the skin lesion images make automatic analysis quite difficult. To overcome these challenges, a highly effective segmentation method based on a fully convolutional network (FCN) is presented in this paper. The proposed improved FCN (iFCN) architecture is used for the segmentation of full-resolution skin lesion images without any pre- or post-processing. It is to support the residual structure of the FCN architecture with spatial information. This situation, which creates a more advanced residual system, enables more precise detection of details on the edges of the lesion, and an analysis independent of skin color can be performed. It offers two contributions: determining the center of the lesion and clarifying the edge details despite the undesirable effects. Two publicly available datasets, the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 Challenge and PH2 datasets, are used to evaluate the performance of the iFCN method. The mean Jaccard index is 78.34%, the mean Dice score is 88.64%, and the mean accuracy value is 95.30% for the proposed method for the ISBI 2017 test dataset. Furthermore, the mean Jaccard index is 87.1%, the mean Dice score is 93.02%, and the mean accuracy value is 96.92% for the proposed method for the PH2 test dataset.

Keywords: CNN; FCN; Melanoma; Segmentation; Skin lesion segmentation.

Fig. 1

Undesirable residues and some problems of skin lesion images. a Brown lesion. b Black hairs. c Black hairs with the brown lesion. d Markers. e White residues on the lesion. f White residues on the lesion. g White and indistinct lesion. h Hard scene for lesion

Fig. 2

Indistinct boundaries. a, b, c, g Lesion images. d, e, f, i Ground truth images of a, b, c, and g. h Boundaries of a skin lesion. j Ground truth of h image

Fig. 3

The proposed iFCN architecture

Fig. 4

Color space effect on lesion images. a Original images. b R component from RGB. c G component from RGB. d B component from RGB. e S component from HSV. f I component from YIQ. g Cb component from YCbCr. h Z component from XYZ

Fig. 5

Adding color components to the iFCN architecture

Fig. 6

The up-sampling effect. a One up-sampling. b Two up-sampling. c Three up-sampling

Fig. 7

Training and validation curves of the iFCN architecture

Fig. 8

The segmentation results of the proposed iFCN architecture. a Ground truth images. b iFCN results. c Plotting the proposed method and ground truth on the lesion