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Proceedings ArticleDOI

A prototype for a mobile-based system of skin lesion analysis using supervised classification

Luas Rosado, +1 more
- pp 156-157
TLDR
The main objective of this work is to create a mobile-based prototype to analyze skin lesions based on supervised classification, which collects, processes and storages information of skin lesions through the automatic extraction and classification of specific visual features.
Abstract
Mobile Teledermatology appears nowadays as a promising tool with the potential to empower patients to adopt an active role in managing their own health status, while facilitates the early diagnosis of skin cancers. The main objective of this work is to create a mobile-based prototype to analyze skin lesions based on supervised classification. The presented self-monitoring system collects, processes and storages information of skin lesions through the automatic extraction and classification of specific visual features. The selected features are based on the ABCD rule, which considers 4 visual criteria considered highly relevant for the detection of malignant melanoma. The algorithms used to extract and classify these features are briefly described, as well as the overall system requirements and architecture.

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Citations
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A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: coherent taxonomy, open issues and recommendation pathway solution

TL;DR: With the exception of the 89 papers reviewed, the new recommendation pathway solution was described in order to improve the measurement process for smartphone-based skin cancer diagnosis applications.
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A Systematic Review on Smartphone Skin Cancer Apps: Coherent Taxonomy, Motivations, Open Challenges and Recommendations, and New Research Direction

TL;DR: This study contributes to this area of research by providing a detailed review of the available options and problems to allow other researchers and participants to further develop skin cancer apps, and the new directions of this research were described.
Journal ArticleDOI

Opportunities and Challenges: Classification of Skin Disease Based on Deep Learning

TL;DR: In this article, the authors provide a quick review of the classification of skin disease using deep learning to summarize the characteristics of skin lesions and the status of image technology, and analyze these studies using datasets, data processing, classification models, and evaluation criteria.
Journal ArticleDOI

A survey, review, and future trends of skin lesion segmentation and classification

TL;DR: A comprehensive literature survey and review of a total of 594 publications (356 for skin lesion segmentation and 238 for skin lesions classification) published between 2011 and 2022 is provided in this article .
Proceedings ArticleDOI

Comprehensive review of techniques used to detect skin lesion

TL;DR: The paper conducts the review of various image processing techniques which are used for diagnosis of skin diseases in recent time and analyses of the different methodologies and their performances.
References
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Journal ArticleDOI

Distribution Free Decomposition of Multivariate Data

TL;DR: The proposed algorithm is stable and efficient, a 10,000 point data set being decomposed in only a few seconds, and convergence of the gradient ascent mean shift procedure is demonstrated for arbitrary distribution and cardinality of the data.
Journal ArticleDOI

Diagnostic accuracy and image quality using a digital camera for teledermatology.

TL;DR: Digital photography for store-and-forward teledermatology produces high-quality images and diagnostic concordance rates that compare favorably with in-person clinical diagnoses.
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