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Glomerulosclerosis detection with pre-trained CNNs ensemble

Author

Listed:
  • Justino Santos

    (Instituto Federal de Educação, Ciência e Tecnologia do Piauí
    Universidade Federal 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)

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%.

Suggested Citation

  • Justino Santos & Romuere Silva & Luciano Oliveira & Washington Santos & Nayze Aldeman & Angelo Duarte & Rodrigo Veras, 2024. "Glomerulosclerosis detection with pre-trained CNNs ensemble," Computational Statistics, Springer, vol. 39(2), pages 561-581, April.
    • Handle: RePEc:spr:compst:v:39:y:2024:i:2:d:10.1007_s00180-022-01307-3
    DOI: 10.1007/s00180-022-01307-3
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