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Orientador(es)
Resumo(s)
Several image processing methods in Dermatology are grounded in shallow and deep learning approaches. These solutions are relevant to assist health experts in decision-making processes related to harmful melanoma—a malignant melanocytic condition—and other skin lesions. This work aims to compare these approaches in a specific classification problem: malignant melanocytic lesions versus non-melanocytic ones. We developed 39 learning method configurations, including three original ones based on fine-tuned deep neural networks. Some implemented settings incorporate auxiliary procedures, such as oversampling, feature selection and data augmentation. An experimental evaluation in the public Derm7pt dermoscopic database suggests that the best original setting performance was competitive against the leading results reported by recent literature alternatives. In particular, the proposal reached average accuracy and sensitivity of 0.9909 and 0.9976, respectively. These results were averaged across three runs of the stratified nested cross-validation strategy. Moreover, our 39 configurations outperformed an experimental baseline derived from the majority class error. Thus, this work can be helpful in inspiring computational systems that could act as preliminary filters to support the detection of a harmful form of skin cancer and its separation from other lesions.
Descrição
We would like to acknowledge the Secretaria de Estado da Ciência, Tecnologia e Ensino Superior—SETI—Fundo Paraná through a research scholarship by the Project 49/2024 to some LABI members. We also thank eurekaSD: Enhancing University Research and Education in Areas Useful for Sustainable Development—Grants EK14AC0037 and EK15AC0264. The Portuguese team was partially supported by Fundação para a Ciência e a Tecnologia (FCT). R Fonseca-Pinto was financed by the Projects UID/05704/2023 and UID/50008/2023, and C V Nogueira was financed by the Project UID/00013/2025. The funding agencies did not have any further involvement in this paper. We also thank PGEEC and PPGCAS at UNIOESTE for their cooperation in this research.
Palavras-chave
Artificial intelligence Biomedical engineering Feature learning Machine learning Statistical test Transfer learning
Contexto Educativo
Citação
Spolaôr, N., Cherman, E. A., Igawa, R. A., Sato, A. K., Nanni, M. R., & Falcão, A. X. (2026). Shallow and deep learning approaches to classify melanoma and non-melanocytic skin lesions. Medical Engineering & Physics, 147, 015014. https://doi.org/10.1088/1873-4030/ac290b
Editora
IOP Publishing