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Early diagnosis of malignant lesions is crucial for reducing mortality. The use of deep learning techniques on dermoscopic images can help in keeping track of the change over time in the appearance of the lesion, which is an important factor for detecting malignant lesions. In this paper, we present a deep learning architecture called Attention Squeeze U-Net for skin lesion area segmentation specifically designed for embedded devices. The main goal is to increase the patient empowerment through the adoption of deep learning algorithms that can run locally on smartphones or low cost embedded devices. This can be the basis to (1) create a history of the lesion, (2) reduce patient visits to the hospital, and (3) protect the privacy of the users. Quantitative results on publicly available data demonstrate that it is possible to achieve good segmentation results even with a compact model.<\/jats:p>","DOI":"10.1007\/s10278-022-00634-7","type":"journal-article","created":{"date-parts":[[2022,5,3]],"date-time":"2022-05-03T21:08:57Z","timestamp":1651612137000},"page":"1217-1230","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Skin Lesion Area Segmentation Using Attention Squeeze U-Net for Embedded Devices"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9081-0765","authenticated-orcid":false,"given":"Andrea","family":"Pennisi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0339-8651","authenticated-orcid":false,"given":"Domenico D.","family":"Bloisi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1199-8358","authenticated-orcid":false,"given":"Vincenzo","family":"Suriani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6606-200X","authenticated-orcid":false,"given":"Daniele","family":"Nardi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4243-2392","authenticated-orcid":false,"given":"Antonio","family":"Facchiano","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9147-4249","authenticated-orcid":false,"given":"Anna Rita","family":"Giampetruzzi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,5,3]]},"reference":[{"key":"634_CR1","doi-asserted-by":"publisher","unstructured":"Bisla, D., Choromanska, A., Berman, R., Stein, J., Polsky, D.: Towards automated melanoma detection with deep learning: Data purification and augmentation. 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We used only data coming from publicly available datasets. In particular, we used the ISIC 2017 and 2018 Challenge datasets and the PH2 dataset.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}