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Journal of Imaging Informatics in Medicine

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Open Access 03-05-2022 | Melanoma | Original Paper

Skin Lesion Area Segmentation Using Attention Squeeze U-Net for Embedded Devices

Authors: Andrea Pennisi, Domenico D. Bloisi, Vincenzo Suriani, Daniele Nardi, Antonio Facchiano, Anna Rita Giampetruzzi

Published in: Journal of Imaging Informatics in Medicine | Issue 5/2022

Abstract

Melanoma is the deadliest form of skin cancer. 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.

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Title: Skin Lesion Area Segmentation Using Attention Squeeze U-Net for Embedded Devices
Authors: Andrea Pennisi
Domenico D. Bloisi
Vincenzo Suriani
Daniele Nardi
Antonio Facchiano
Anna Rita Giampetruzzi
Publication date: 03-05-2022
Publisher: Springer International Publishing
Keywords: Melanoma
Melanoma
Published in: Journal of Imaging Informatics in Medicine / Issue 5/2022
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI: https://doi.org/10.1007/s10278-022-00634-7

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Skin Lesion Area Segmentation Using Attention Squeeze U-Net for Embedded Devices

Abstract

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

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