Classification Models for Skin Tumor Detection Using Texture Analysis in Medical Images

Autor: Marcos A. M. Almeida, Iury A. X. Santos

Publikováno v: Journal of Imaging, Vol 6, Iss 6, p 51 (2020)

Medical images have made a great contribution to early diagnosis. In this study, a new strategy is presented for analyzing medical images of skin with melanoma and nevus to model, classify and identify lesions on the skin. Machine learning applied to

Externí odkaz: https://doaj.org/article/10c006d2c1224ee8a4cc6dacb9302850

Classification Models for Skin Tumor Detection Using Texture Analysis in Medical Images

Autor: Iury A. X. Santos, Marcos A. M. Almeida

Publikováno v: Journal of Imaging
Volume 6
Issue 6
Journal of Imaging, Vol 6, Iss 51, p 51 (2020)

Medical images have made a great contribution to early diagnosis. In this study, a new strategy is presented for analyzing medical images of skin with melanoma and nevus to model, classify and identify lesions on the skin. Machine learning applied to

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0a150ca5256af68bf62f12fffbfebce9
https://doi.org/10.3390/jimaging6060051

Nuclear entropy, angular second moment, variance and texture correlation of thymus cortical and medullar lymphocytes: Grey level co-occurrence matrix analysis

Autor: Senka Pantic, Milan Perovic, Igor Pantic, Jovanna Paunovic

Publikováno v: Anais da Academia Brasileira de Ciências, Volume: 85, Issue: 3, Pages: 1063-1072, Published: 13 AUG 2013
Anais da Academia Brasileira de Ciências, Vol 85, Iss 3, Pp 1063-1072 (2013)
Anais da Academia Brasileira de Ciências v.85 n.3 2013
Anais da Academia Brasileira de Ciências
Academia Brasileira de Ciências (ABC)
instacron:ABC

Grey level co-occurrence matrix analysis (GLCM) is a well-known mathematical method for quantification of cell and tissue textural properties, such as homogeneity, complexity and level of disorder. Recently, it was demonstrated that this method is ca

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b7bc99cb2ea3bc4646046e1aeae4cd2c
https://doi.org/10.1590/s0001-37652013005000045

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