Proceedings ArticleDOI
A prototype for a mobile-based system of skin lesion analysis using supervised classification
Luas Rosado,Marcia Ferreira +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.read more
Citations
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Journal ArticleDOI
A review on smartphone skin cancer diagnosis apps in evaluation and benchmarking: coherent taxonomy, open issues and recommendation pathway solution
A. A. Zaidan,B. B. Zaidan,Osamah Shihab Albahri,M. A. Alsalem,Ahmed Shihab Albahri,Qahtan M. Yas,M. Hashim +6 more
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.
Journal ArticleDOI
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
Navneet Singh,Prabhpreet Kaur +1 more
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 Article
ABCD rule of dermatoscopy : a new practical method for early recognition of malignant melanoma
Journal ArticleDOI
Distribution Free Decomposition of Multivariate Data
Dorin Comaniciu,Peter Meer +1 more
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.
Elizabeth A. Krupinski,Ben LeSueur,Lansing Ellsworth,Norman Levine,Ronald C. Hansen,Nancy G. Silvis,Peter Sarantopoulos,Pamela Hite,James P. Wurzel,Ronald S. Weinstein,Ana Maria Lopez +10 more
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.
Journal ArticleDOI
Automatic detection of blue-white veil and related structures in dermoscopy images
M. Emre Celebi,Hitoshi Iyatomi,William V. Stoecker,Randy Hays Moss,Harold S. Rabinovitz,Giuseppe Argenziano,H. Peter Soyer +6 more
TL;DR: A machine learning approach to the detection of blue-white veil and related structures in dermoscopy images is presented, which involves contextual pixel classification using a decision tree classifier.
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