Name
Abstract | ||
|---|---|---|
In the medical field, melanoma is one of the most dangerous skin cancers. However, the accuracy rate of doctors’ identification of melanoma is only 60%. Diagnosis requires higher technical experience and low fault tolerance for doctors who identify melanoma and other skin lesions. Therefore, the accurate segmentation of melanoma is of vital importance for clinical diagnosis and treatment. The current segmentation of melanoma is mainly based on fully connected networks (FCNs) and U-Net. Nevertheless, these two kinds of neural networks are prone to parameter redundancy, and the gradient disappears when depth increases, which reduces the Jaccard index of the skin lesion image segmentation model. To solve the above problems and improve the survival rate of melanoma patients, this paper proposes an improved skin lesion segmentation model based on U-Net++. In particular, we introduce a new loss function, which improves the Jaccard index of skin lesion image segmentation. The experiments show that our model has excellent performance on the segmentation of the ISIC2018 Task I dataset, and achieves a Jaccard index of 84.73%. The proposed method improves the Jaccard index of segmentation of skin lesion images and could also assist dermatological doctors in determining and diagnosing the types of skin lesions and the boundary between lesions and normal skin. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s11042-022-12067-z | Multimedia Tools and Applications |
Keywords | DocType | Volume |
U-Net++, Melanoma, Skin lesion segmentation, Biomedical image processing, Convolutional neural networks, Dermoscopic images, Artificial intelligence, Jaccard index | Journal | 81 |
Issue | ISSN | Citations |
6 | 1380-7501 | 0 |
PageRank | References | Authors |
0.34 | 11 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhao Chen | 1 | 76 | 25.75 |
| Renjun Shuai | 2 | 0 | 0.68 |
| Li Ma | 3 | 0 | 0.68 |
| Wenjia Liu | 4 | 0 | 0.34 |
| Menglin Wu | 5 | 0 | 0.68 |