Please use this identifier to cite or link to this item: https://hdl.handle.net/10216/135741
Author(s): Justino Duarte Santos
Rodrigo de M. S. Veras
Romuere R. V. Silva
Nayze L. S. Aldeman
Flávio H. D. Araújo
Angelo A. Duarte
João Manuel R. S. Tavares
Title: A hybrid of deep and textural features to differentiate glomerulosclerosis and minimal change disease from glomerulus biopsy images
Issue Date: 2021-09
Abstract: 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.
Subject: Ciências Tecnológicas, Ciências médicas e da saúde
Technological sciences, Medical and Health sciences
Scientific areas: Ciências médicas e da saúde
Medical and Health sciences
URI: https://hdl.handle.net/10216/135741
Document Type: Artigo em Revista Científica Internacional
Rights: openAccess
Appears in Collections:FEUP - Artigo em Revista Científica Internacional

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