HWA-SegNet: Multi-channel skin lesion image segmentation network with hierarchical analysis and weight adjustment

Convolutional neural networks (CNNs) show excellent performance in accurate medical image segmentation. However, the characteristics of sample with small size and insufficient feature expression, irregular shape of the segmented target and inaccurate judgment of edge texture have always been problems to be faced in the field of skin lesion image segmentation. Therefore, in order to solve these problems, discrete Fourier transform (DFT) is introduced to enrich the input data and a CNN architecture (HWA-SegNet) is proposed in this paper. Firstly, DFT is improved to analyze the features of the skin lesions image, and multi-channel data is extended for each image. Secondly, a hierarchical dilated analysis module is constructed to understand the semantic features under multi-channel. Finally, the pre-prediction results are fine-tuned using a weight adjustment structure with fully connected layers to obtain higher accuracy prediction results. Then, 520 skin lesion images are tested on the ISIC 2018 dataset. Extensive experimental results show that our HWA-SegNet improve the average segmentation Dice Similarity Coefficient from 88.30% to 91.88%, Sensitivity from 89.29% to 92.99%, and Jaccard similarity index from 81.15% to 85.90% compared with U-Net. Compared with the State-of-the-Art method, the Jaccard similarity index and Specificity are close, but the Dice Similarity Coefficient is higher. The experimental data show that the data augmentation strategy based on improved DFT and HWA-SegNet are effective for skin lesion image segmentation.

Automated summary

This paper proposes a method based on improved discrete Fourier transform (DFT) and CNN architecture (HWA-SegNet) to solve the problems of small sample size, insufficient feature expression, irregular shape of segmented target, and inaccurate judgment of edge texture in skin lesion image segmentation. Experimental results show that the proposed method achieves higher accuracy compared to other methods in skin lesion image segmentation.