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Updated: May 13, 2025

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Multi-level feature fusion network for kidney disease detection.

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  • 1School of Computer Science and Engineering, Central South University, 932 Lushan S Rd, Yuelu District, Changsha, Hunan, China.

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|April 15, 2025
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A novel deep learning framework automates kidney disease detection using feature fusion and sequential modeling. This approach enhances diagnostic accuracy, offering a reliable solution for early identification and clinical decision-making.

Keywords:
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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Nephrology

Background:

  • Kidney irregularities present a significant public health concern, often leading to severe complications.
  • Limited availability of nephrologists hinders timely and cost-effective early detection of kidney diseases.

Purpose of the Study:

  • To develop and validate a deep learning framework for automated kidney disease detection.
  • To enhance diagnostic accuracy by integrating feature fusion and sequential modeling techniques.

Main Methods:

  • Evaluation of six pretrained models (ResNet50, VGG19 identified for feature extraction).
  • Integration of feature fusion with an inception block for diverse feature representation.
  • Incorporation of Convolutional Long Short-Term Memory (ConvLSTM) for sequential learning and an additional Inception block for refined feature extraction.
  • Validation on a new Multiple Hospital Collected (MHC-CT) dataset (1860 tumor, 1024 normal kidney CT scans) and a public multiclass CT dataset.

Main Results:

  • Achieved 99.60% accuracy on the MHC-CT dataset for binary classification.
  • Attained 91.31% accuracy on a public multiclass CT scan dataset, demonstrating generalization capability.
  • Superior performance attributed to effective feature fusion (inception blocks) and ConvLSTM's sequential learning for enhanced spatial and temporal patterns.

Conclusions:

  • The proposed deep learning framework effectively automates kidney disease detection.
  • The integration of feature fusion and sequential modeling provides a robust and efficient solution for clinical decision-making.
  • This approach addresses the challenges of early kidney disease detection, improving accessibility and accuracy.