Fig 1.
(a) Background differences between skin and lesions with poor contrast. (b) Irregularity and variable size of lesions. (c) Images of lesions are often accompanied by a large amount of noise. The green line represents Ground truth and the yellow is segmented by our model.
Fig 2.
Pyramid residual attention network.
Table 1.
List of parameters of PRA modules in each position in the model, where ( ) represents the (1, 3, 5, 7) four convolution kernels with the same channels.
Fig 3.
Pyramid Residual Attention Module.
Fig 4.
Image display using data enhancement, (a) original image (b) normalized and data enhanced image.
Fig 5.
Precision-Recall curve and ROC curve for the effectiveness of each component of the PRA module.
Table 2.
Table of experimental results of the effectiveness ablation of each component of PRA module.
The PM, CA, Res stands for Pyramid Module, Channel Attention, Residual Unit, respectively, “-” refers to replace the module with a 3×3 convolution kernel. Bold data indicates the maximum value in this indicator.
Table 3.
Validation table for PRA module made up in horizontal and vertical directions.
Fig 6.
The segmentation results of the proposed PRA module and PRAN test on the ISIC2018 dataset.
Here, (a) is one of the images, the green line represents Ground Truth, and the yellow line represents the segmentation result of PRAN. (b) shows the labeled graph of Ground Truth, (c), (d), (e) is the segmentation result of PRAN, the horizontal combination of two PRA modules, and a single PRA module, respectively.
Fig 7.
The Plot of PR curves for the combined ablation experiment, where PRA is a single module, the shallow layer of the PRAN is that has been combined horizontally, and PRAN is the complete network after assembling of PRA modules vertically.
Fig 8.
The Plot of train and valid loss curves for the combined ablation experiment.
PRA is a single module, the shallow layer of the PRAN is that has been combined horizontally, and PRAN is the complete network after assembling of PRA modules vertically.
Fig 9.
Visualization results of segmentation on ISIC2017, the discolored area represents the segmentation result of the model or label, and the darker part of the segmented lesion area indicates the part of the original image where the skin color is obvious.
Table 4.
Comparison of PRAN with existing models on ISIC2017 dataset. Bold data in the table indicates the highest metric in the column.
Fig 10.
Visualization results of segmentation on ISIC2018, the discolored part represents the segmentation result of the lesion area on the original image, and the darker part of the segmented lesion area indicates the part of the original image where the skin color is obviously.
Table 5.
Comparison table of segmentation performance on the ISIC2018 dataset.
Fig 11.
Visualization results of segmentation on KvasirSEG dataset.
The discolored part represents the segmentation result of the lesion area on the original image. The darker part of the segmented lesion area indicates the part of the original image where the skin color is clear.
Table 6.
Comparison test table of generalizability of the proposed model on KvasirSEG dataset.
Bold data in the table indicates the highest metric in the column.