{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T18:02:20Z","timestamp":1777917740283,"version":"3.51.4"},"reference-count":37,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,11]],"date-time":"2022-11-11T00:00:00Z","timestamp":1668124800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"This research was funded by the National Key Research and Development Program of China","award":["2019YFC0117800"],"award-info":[{"award-number":["2019YFC0117800"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Melanoma is a main factor that leads to skin cancer, and early diagnosis and treatment can significantly reduce the mortality of patients. Skin lesion boundary segmentation is a key to accurately localizing a lesion in dermoscopic images. However, the irregular shape and size of the lesions and the blurred boundary of the lesions pose significant challenges for researchers. In recent years, pixel-level semantic segmentation strategies based on convolutional neural networks have been widely used, but many methods still suffer from the inaccurate segmentation of fuzzy boundaries. In this paper, we proposed a multi-scale hybrid attentional convolutional neural network (MHAU-Net) for the precise localization and segmentation of skin lesions. MHAU-Net has four main components: multi-scale resolution input, hybrid residual attention (HRA), dilated convolution, and atrous spatial pyramid pooling. Multi-scale resolution inputs provide richer visual information, and HRA solves the problem of blurred boundaries and enhances the segmentation results. The Dice, mIoU, average specificity, and sensitivity on the ISIC2018 task 1 validation set were 93.69%, 90.02%, 92.7% and 93.9%, respectively. The segmentation metrics are significantly better than the latest DCSAU-Net, UNeXt, and U-Net, and excellent segmentation results are achieved on different datasets. We performed model robustness validations on the Kvasir-SEG dataset with an overall sensitivity and average specificity of 95.91% and 96.28%, respectively.<\/jats:p>","DOI":"10.3390\/s22228701","type":"journal-article","created":{"date-parts":[[2022,11,11]],"date-time":"2022-11-11T01:39:54Z","timestamp":1668130794000},"page":"8701","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["MHAU-Net: Skin Lesion Segmentation Based on Multi-Scale Hybrid Residual Attention Network"],"prefix":"10.3390","volume":"22","author":[{"given":"Yingjie","family":"Li","sequence":"first","affiliation":[{"name":"School of Integrated Circuits, Anhui University, Hefei 230601, China"},{"name":"Anhui Engineering Laboratory of Agro-Ecological Big Data, Hefei 230601, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Anhui University, Hefei 230601, China"},{"name":"Anhui Engineering Laboratory of Agro-Ecological Big Data, Hefei 230601, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jubao","family":"Han","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Anhui University, Hefei 230601, China"},{"name":"Anhui Engineering Laboratory of Agro-Ecological Big Data, Hefei 230601, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziheng","family":"An","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Anhui University, Hefei 230601, China"},{"name":"Anhui Engineering Laboratory of Agro-Ecological Big Data, Hefei 230601, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deyu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Anhui University, Hefei 230601, China"},{"name":"Anhui Engineering Laboratory of Agro-Ecological Big Data, Hefei 230601, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haichao","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Anhui University, Hefei 230601, China"},{"name":"Anhui Engineering Laboratory of Agro-Ecological Big Data, Hefei 230601, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuanxu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits, Anhui University, Hefei 230601, China"},{"name":"Anhui Engineering Laboratory of Agro-Ecological Big Data, Hefei 230601, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"820","DOI":"10.1109\/JPROC.2021.3054390","article-title":"A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises","volume":"109","author":"Zhou","year":"2021","journal-title":"Proc. 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