A hybrid of deep and textural features to differentiate glomerulosclerosis and minimal change disease from glomerulus biopsy images

• Tipo: Artigo em Revista Científica Internacional
• Data: 2021-09

Autores

Justino Duarte Santos (Autor) Outra; Rodrigo de M. S. Veras (Autor) Outra; Romuere R. V. Silva (Autor) Outra; Nayze L. S. Aldeman (Autor) Outra; Flávio H. D. Araújo (Autor) Outra; Angelo A. Duarte (Autor) Outra; João Manuel R. S. Tavares (Autor) FEUP

Revista

Título: Biomedical Signal Processing and Control

Vol. 70 Nº 103020

Páginas: 1-9

ISSN: 1746-8094

Editora: Elsevier

Indexação

ISI Web of Knowledge - 0 Citações

ISI Web of Science

Scopus - 0 Citações

Classificação Científica

FOS: Ciências médicas e da saúde

CORDIS: Ciências Tecnológicas

Outras Informações

ID Authenticus: P-00V-A46

DOI: 10.1016/j.bspc.2021.103020

Abstract (EN): The minimal change disease (MCD) and glomerulosclerosis (GS) are two common kidney diseases. Unless adequately treated, these diseases leads to chronic kidney diseases. Accurate differentiation of these two diseases is of paramount importance as their methods of treatment and prognoses are different. Thus, this article propose a method capable of differentiating MCD from GS in glomerulus biopsies images based on a new hybrid deep and texture feature space. We conducted an extensive study to determine the best set of features for image representation. Our feature extraction methodology, which includes Haraliks and geostatistics texture descriptors and pre-trained CNNs, resulted in 13,476 characteristics. We then used mutual information to order the elements by importance and select the best set for differentiating MCD from GS using the random forest classifier. The proposed method achieved an accuracy of 90.3% and a Kappa index of 80.5%. Representation of glomerulus biopsy images with a hybrid of deep and textural features facilitates the accurate differentiation of GS and MCD.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 9