PAD-UFES-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones.

Autor:	Pacheco AGC; Graduate Program in Computer Science, Federal University of Espírito Santo, Vitória, Brazil.; Nature Inspired Computing Laboratory, Federal University of Espírito Santo, Vitória, Brazil., Lima GR; Dermatological and Surgical Assistance Program (PAD), Federal University of Espírito Santo, Vitória, Brazil.; Faculty of Medicine, Federal University of Espírito Santo, Vitória, Brazil., Salomão AS; Dermatological and Surgical Assistance Program (PAD), Federal University of Espírito Santo, Vitória, Brazil.; Faculty of Medicine, Federal University of Espírito Santo, Vitória, Brazil., Krohling B; Nature Inspired Computing Laboratory, Federal University of Espírito Santo, Vitória, Brazil., Biral IP; Dermatological and Surgical Assistance Program (PAD), Federal University of Espírito Santo, Vitória, Brazil.; Faculty of Medicine, Federal University of Espírito Santo, Vitória, Brazil., de Angelo GG; Nature Inspired Computing Laboratory, Federal University of Espírito Santo, Vitória, Brazil., Alves FCR Jr; Dermatological and Surgical Assistance Program (PAD), Federal University of Espírito Santo, Vitória, Brazil.; Faculty of Medicine, Federal University of Espírito Santo, Vitória, Brazil., Esgario JGM; Graduate Program in Computer Science, Federal University of Espírito Santo, Vitória, Brazil.; Nature Inspired Computing Laboratory, Federal University of Espírito Santo, Vitória, Brazil., Simora AC; Dermatological and Surgical Assistance Program (PAD), Federal University of Espírito Santo, Vitória, Brazil.; Faculty of Medicine, Federal University of Espírito Santo, Vitória, Brazil., Castro PBC; Nature Inspired Computing Laboratory, Federal University of Espírito Santo, Vitória, Brazil., Rodrigues FB; Nature Inspired Computing Laboratory, Federal University of Espírito Santo, Vitória, Brazil., Frasson PHL; Department of Specialized Medicine, Federal University of Espírito Santo, Vitória, Brazil.; Dermatological and Surgical Assistance Program (PAD), Federal University of Espírito Santo, Vitória, Brazil., Krohling RA; Graduate Program in Computer Science, Federal University of Espírito Santo, Vitória, Brazil.; Nature Inspired Computing Laboratory, Federal University of Espírito Santo, Vitória, Brazil.; Production Engineering Department, Federal University of Espírito Santo, Vitória, Brazil., Knidel H; Nature Inspired Computing Laboratory, Federal University of Espírito Santo, Vitória, Brazil., Santos MCS; Pathological Anatomy Unit of the University Hospital Cassiano Antônio Moraes (HUCAM), Federal University of Espírito Santo, Vitória, Brazil., do Espírito Santo RB; Secretary of Health of the Espírito Santo state, Governor of Espírito Santo state, Vitória, Brazil.; Dermatological and Surgical Assistance Program (PAD), Federal University of Espírito Santo, Vitória, Brazil., Macedo TLSG; Secretary of Health of the Espírito Santo state, Governor of Espírito Santo state, Vitória, Brazil.; Dermatological and Surgical Assistance Program (PAD), Federal University of Espírito Santo, Vitória, Brazil., Canuto TRP; Secretary of Health of the Espírito Santo state, Governor of Espírito Santo state, Vitória, Brazil.; Dermatological and Surgical Assistance Program (PAD), Federal University of Espírito Santo, Vitória, Brazil., de Barros LFS; Dermatological and Surgical Assistance Program (PAD), Federal University of Espírito Santo, Vitória, Brazil.
Jazyk:	angličtina
Zdroj:	Data in brief [Data Brief] 2020 Aug 25; Vol. 32, pp. 106221. Date of Electronic Publication: 2020 Aug 25 (Print Publication: 2020).
DOI:	10.1016/j.dib.2020.106221
Abstrakt:	Over the past few years, different Computer-Aided Diagnosis (CAD) systems have been proposed to tackle skin lesion analysis. Most of these systems work only for dermoscopy images since there is a strong lack of public clinical images archive available to evaluate the aforementioned CAD systems. To fill this gap, we release a skin lesion benchmark composed of clinical images collected from smartphone devices and a set of patient clinical data containing up to 21 features. The dataset consists of 1373 patients, 1641 skin lesions, and 2298 images for six different diagnostics: three skin diseases and three skin cancers. In total, 58.4% of the skin lesions are biopsy-proven, including 100% of the skin cancers. By releasing this benchmark, we aim to support future research and the development of new tools to assist clinicians to detect skin cancer. Competing Interests: The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article. (© 2020 The Author(s).)
Databáze:	MEDLINE
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