Publication
Dermoscopic assisted diagnosis in melanoma: Reviewing results, optimizing methodologies and quantifying empirical guidelines
| dc.contributor.author | Lee, Huei Diana | |
| dc.contributor.author | Mendes, Ana Isabel | |
| dc.contributor.author | Spolaôr, Newton | |
| dc.contributor.author | Oliva, Jefferson Tales | |
| dc.contributor.author | Parmezan, Antonio Rafael Sabino | |
| dc.contributor.author | Wu, Feng Chung | |
| dc.contributor.author | Fonseca-Pinto, Rui | |
| dc.date.accessioned | 2025-05-08T16:48:30Z | |
| dc.date.available | 2025-05-08T16:48:30Z | |
| dc.date.issued | 2018-10-15 | |
| dc.description.abstract | Early diagnosis is still the most important factor to deal with skin cancer, a disease that challenges physicians and researchers. It has benefited from computer-aided diagnosis methods that successfully combine dermoscopy, Digital Image Processing, and Machine Learning techniques. This paper aims to approximate medical professionals working with dermoscopy to these methods, to join the challenge of melanoma early detection. Accordingly, a proposal for extracting, selecting and combining texture and shape features from dermoscopic images is presented. The Feature Selection task is added to the learning process to potentiate the quality of classification models. Three classical Machine Learning algorithms were applied to differentiate melanoma from non-melanoma images. The models are evaluated by standard performance measures and a multi-criteria decision analysis method. This is the first time such method is used in melanoma diagnosis. As a result, we found a decision tree that performs well and allows the explicit representation and analysis of the knowledge learned from the images. In addition, the competitiveness of our decision models in comparison with literature approaches reviewed in this work encourages further applications of Machine Learning and Feature Selection to assist computer-aided diagnosis. | eng |
| dc.description.sponsorship | We would like to acknowledge EurekaSD: Enhancing University Research and Education in Areas Useful for Sustainable Development - grants EK14AC0037 and EK15AC0264. We would like to thank Araucária Foundation for the Support of the Scientific and Technological Development of Paraná through a Research and Technological Productivity Scholarship for H. D. Lee (grant 534/2014). The Portuguese team was partially supported by Fundação para a Ciência e a Tecnologia (FCT), Portugal, project DERMOPLENO, in the scope of R&D Unit (UID/EEA/50008/2013) through national funds and where applicable co-funded by FEDER - PT2020 partnership agreement. We also would like to thank PGEEC/UNIOESTE through a postdoctoral scholarship for N. Spolaôr, the Brazilian National Council for Scientific and Technological Development (CNPq) through the grant 140159/2017-7 for A. R. S. Parmezan and the Coordination for the Improvement of Higher Education Personnel (CAPES) through a Ph.D. scholarship for J. T. Oliva. These agencies did not have any further involvement in this paper. The authors thank J. G. Martins for his help in LBP implementation | |
| dc.identifier.citation | Huei Diana Lee, Ana Isabel Mendes, Newton Spolaôr, Jefferson Tales Oliva, Antonio Rafael Sabino Parmezan, Feng Chung Wu, Rui Fonseca-Pinto, Dermoscopic assisted diagnosis in melanoma: Reviewing results, optimizing methodologies and quantifying empirical guidelines, Knowledge-Based Systems, Volume 158, 2018, Pages 9-24, ISSN 0950-7051, https://doi.org/10.1016/j.knosys.2018.05.016. | |
| dc.identifier.doi | https://doi.org/10.1016/j.knosys.2018.05.016 | |
| dc.identifier.issn | 1872-7409 | |
| dc.identifier.uri | http://hdl.handle.net/10400.8/12861 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | Elsevier | |
| dc.relation | UID/EEA/50008/2013 | |
| dc.relation.hasversion | https://www.sciencedirect.com/science/article/pii/S0950705118302454 | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Image analysis | |
| dc.subject | Dermoscopy | |
| dc.subject | Computer-aided diagnosis | |
| dc.subject | Data mining | |
| dc.subject | Machine learning | |
| dc.title | Dermoscopic assisted diagnosis in melanoma: Reviewing results, optimizing methodologies and quantifying empirical guidelines | eng |
| dc.type | research article | |
| dspace.entity.type | Publication | |
| oaire.citation.title | Knowledge-Based Systems | |
| oaire.citation.volume | 158 | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 |