Res-CDD-Net: A Network with Multi-Scale Attention and Optimized Decoding Path for Skin Lesion Segmentation
Melanoma is a lethal skin cancer. In its diagnosis, skin lesion segmentation plays a critical role. However, skin lesions exhibit a wide range of sizes, shapes, colors, and edges. This makes skin lesion segmentation a challenging task. In this paper, we propose an encoding–decoding network called Re...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/17/2672 |
| _version_ | 1797495914965762048 |
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| author | Zian Song Wenjie Luo Qingxuan Shi |
| author_facet | Zian Song Wenjie Luo Qingxuan Shi |
| author_sort | Zian Song |
| collection | DOAJ |
| description | Melanoma is a lethal skin cancer. In its diagnosis, skin lesion segmentation plays a critical role. However, skin lesions exhibit a wide range of sizes, shapes, colors, and edges. This makes skin lesion segmentation a challenging task. In this paper, we propose an encoding–decoding network called Res-CDD-Net to address the aforementioned aspects related to skin lesion segmentation. First, we adopt ResNeXt50 pre-trained on the ImageNet dataset as the encoding path. This pre-trained ResNeXt50 can provide rich image features to the whole network to achieve higher segmentation accuracy. Second, a channel and spatial attention block (CSAB), which integrates both channel and spatial attention, and a multi-scale capture block (MSCB) are introduced between the encoding and decoding paths. The CSAB can highlight the lesion area and inhibit irrelevant objects. MSCB can extract multi-scale information to learn lesion areas of different sizes. Third, we upgrade the decoding path. Every 3 × 3 square convolution kernel in the decoding path is replaced by a diverse branch block (DBB), which not only promotes the feature restoration capability, but also improves the performance and robustness of the network. We evaluate the proposed network on three public skin lesion datasets, namely ISIC-2017, ISIC-2016, and PH2. The dice coefficient is 6.90% higher than that of U-Net, whereas the Jaccard index is 10.84% higher than that of U-Net (assessed on the ISIC-2017 dataset). The results show that Res-CDD-Net achieves outstanding performance, higher than the performance of most state-of-the-art networks. Last but not least, the training of the network is fast, and good results can be achieved in early stages of training. |
| first_indexed | 2024-03-10T01:56:25Z |
| format | Article |
| id | doaj.art-e5fc2ecf8aeb4d1f9d056a3999f2d874 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T01:56:25Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-e5fc2ecf8aeb4d1f9d056a3999f2d8742023-11-23T12:57:23ZengMDPI AGElectronics2079-92922022-08-011117267210.3390/electronics11172672Res-CDD-Net: A Network with Multi-Scale Attention and Optimized Decoding Path for Skin Lesion SegmentationZian Song0Wenjie Luo1Qingxuan Shi2School of Cybersecurity and Computer, Hebei University, Baoding 071002, ChinaSchool of Cybersecurity and Computer, Hebei University, Baoding 071002, ChinaSchool of Cybersecurity and Computer, Hebei University, Baoding 071002, ChinaMelanoma is a lethal skin cancer. In its diagnosis, skin lesion segmentation plays a critical role. However, skin lesions exhibit a wide range of sizes, shapes, colors, and edges. This makes skin lesion segmentation a challenging task. In this paper, we propose an encoding–decoding network called Res-CDD-Net to address the aforementioned aspects related to skin lesion segmentation. First, we adopt ResNeXt50 pre-trained on the ImageNet dataset as the encoding path. This pre-trained ResNeXt50 can provide rich image features to the whole network to achieve higher segmentation accuracy. Second, a channel and spatial attention block (CSAB), which integrates both channel and spatial attention, and a multi-scale capture block (MSCB) are introduced between the encoding and decoding paths. The CSAB can highlight the lesion area and inhibit irrelevant objects. MSCB can extract multi-scale information to learn lesion areas of different sizes. Third, we upgrade the decoding path. Every 3 × 3 square convolution kernel in the decoding path is replaced by a diverse branch block (DBB), which not only promotes the feature restoration capability, but also improves the performance and robustness of the network. We evaluate the proposed network on three public skin lesion datasets, namely ISIC-2017, ISIC-2016, and PH2. The dice coefficient is 6.90% higher than that of U-Net, whereas the Jaccard index is 10.84% higher than that of U-Net (assessed on the ISIC-2017 dataset). The results show that Res-CDD-Net achieves outstanding performance, higher than the performance of most state-of-the-art networks. Last but not least, the training of the network is fast, and good results can be achieved in early stages of training.https://www.mdpi.com/2079-9292/11/17/2672skin lesion segmentationencoding–decoding networkattention mechanismmulti-scale feature fusiondiverse branch block |
| spellingShingle | Zian Song Wenjie Luo Qingxuan Shi Res-CDD-Net: A Network with Multi-Scale Attention and Optimized Decoding Path for Skin Lesion Segmentation Electronics skin lesion segmentation encoding–decoding network attention mechanism multi-scale feature fusion diverse branch block |
| title | Res-CDD-Net: A Network with Multi-Scale Attention and Optimized Decoding Path for Skin Lesion Segmentation |
| title_full | Res-CDD-Net: A Network with Multi-Scale Attention and Optimized Decoding Path for Skin Lesion Segmentation |
| title_fullStr | Res-CDD-Net: A Network with Multi-Scale Attention and Optimized Decoding Path for Skin Lesion Segmentation |
| title_full_unstemmed | Res-CDD-Net: A Network with Multi-Scale Attention and Optimized Decoding Path for Skin Lesion Segmentation |
| title_short | Res-CDD-Net: A Network with Multi-Scale Attention and Optimized Decoding Path for Skin Lesion Segmentation |
| title_sort | res cdd net a network with multi scale attention and optimized decoding path for skin lesion segmentation |
| topic | skin lesion segmentation encoding–decoding network attention mechanism multi-scale feature fusion diverse branch block |
| url | https://www.mdpi.com/2079-9292/11/17/2672 |
| work_keys_str_mv | AT ziansong rescddnetanetworkwithmultiscaleattentionandoptimizeddecodingpathforskinlesionsegmentation AT wenjieluo rescddnetanetworkwithmultiscaleattentionandoptimizeddecodingpathforskinlesionsegmentation AT qingxuanshi rescddnetanetworkwithmultiscaleattentionandoptimizeddecodingpathforskinlesionsegmentation |