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

• Type: Article in International Scientific Journal
• Date: 2021-09

Authors

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

Journal

Title: Biomedical Signal Processing and Control

Vol. 70 No. 103020

Pages: 1-9

ISSN: 1746-8094

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 0 Citations

ISI Web of Science

Scopus - 0 Citations

Scientific classification

FOS: Medical and Health sciences

CORDIS: Technological sciences

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 9