Skin Lesion Segmentation and Multiclass Classification Using Deep Learning Features and Improved Moth Flame Optimization

Manual diagnosis of skin cancer is time-consuming and expensive; therefore, it is essential to develop automated diagnostics methods with the ability to classify multiclass skin lesions with greater accuracy. We propose a fully automated approach for multiclass skin lesion segmentation and classific...

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Detalles Bibliográficos
Autores principales: Khan, Muhammad Attique, Sharif, Muhammad, Akram, Tallha, Damaševičius, Robertas, Maskeliūnas, Rytis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145295/
https://www.ncbi.nlm.nih.gov/pubmed/33947117
http://dx.doi.org/10.3390/diagnostics11050811

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8145295/
https://www.ncbi.nlm.nih.gov/pubmed/33947117
http://dx.doi.org/10.3390/diagnostics11050811

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