Glomerulosclerosis detection with pre-trained CNNs ensemble

Justino Santos (), Romuere Silva (), Luciano Oliveira (), Washington Santos (), Nayze Aldeman (), Angelo Duarte () and Rodrigo Veras ()
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Justino Santos: Instituto Federal de Educação, Ciência e Tecnologia do Piauí
Romuere Silva: Universidade Federal do Piauí
Luciano Oliveira: Universidade Federal da Bahia
Washington Santos: Fundação Oswaldo Cruz
Nayze Aldeman: Universidade Federal do Delta do Parnaíba
Angelo Duarte: Universidade Estadual de Feira de Santana
Rodrigo Veras: Universidade Federal do Piauí

Computational Statistics, 2024, vol. 39, issue 2, No 7, 581 pages

Abstract: Abstract Glomerulosclerosis characterizes many conditions of primary kidney disease in advanced stages. Its accurate diagnosis relies on histological analysis of renal cortex biopsy, and it is paramount to guide the appropriate treatment and minimize the chances of the disease progressing to chronic stages. This article presents an ensemble approach composed of five convolutional neural networks (CNNs) - VGG-19, Inception-V3, ResNet-50, DenseNet-201, and EfficientNet-B2 - to detect glomerulosclerosis in glomerulus images. We fine-tuned the CNNs and evaluated several configurations for the fully connected layers. In total, we analyzed 25 different models. These CNNs, individually, demonstrated effectiveness in the task; however, we verified that the union of these five well-known CNNs improved the detection rate while decreasing the standard deviations of current techniques. The experiments were carried out in a data set comprised of 1,028 images, on which we applied data-augmentation techniques in the training set. The proposed CNNs ensemble achieved a near-perfect accuracy of 99.0% and kappa of 98.0%.

Keywords: Transfer learning; Kidney disease; Computer-aided diagnosis; Image analysis (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00180-022-01307-3

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