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Related Experiment Video

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DSNN: A DenseNet-Based SNN for Explainable Brain Disease Classification.

Ziquan Zhu1, Siyuan Lu1, Shui-Hua Wang1,2

  • 1School of Computing and Mathematical Sciences, University of Leicester, East Midlands, United Kingdom.

Frontiers in Systems Neuroscience
|June 20, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces the DenseNet-based SNN (DSNN) model for classifying brain diseases, offering improved accuracy and overcoming limitations of traditional machine learning algorithms. The DSNN model demonstrates superior performance in identifying various brain conditions.

Keywords:
DenseNetMRIbrain diseasesconvolutional neural networkrandomized neural network

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Area of Science:

  • Neurology
  • Artificial Intelligence
  • Medical Imaging Analysis

Background:

  • Brain diseases encompass a wide range of conditions including inflammation, tumors, and neurodegenerative disorders like Alzheimer's disease (AD).
  • The prevalence of AD is increasing, with significant projected growth in elderly populations, highlighting the need for advanced diagnostic tools.
  • Traditional machine learning models, such as convolutional neural networks (CNNs), often require extensive training data and can suffer from overfitting.

Purpose of the Study:

  • To address the limitations of existing machine learning models in brain disease classification.
  • To propose novel models utilizing randomized neural networks (RNNs) integrated with DenseNet for enhanced classification performance.
  • To evaluate the efficacy of DenseNet-based SNN (DSNN), DenseNet-based RVFL (DRVFL), and DenseNet-based ELM (DELM) for brain disease diagnosis.

Main Methods:

  • Developed three novel models: DSNN, DRVFL, and DELM, using a pre-trained DenseNet backbone.
  • Fine-tuned the modified DenseNet on an empirical dataset.
  • Replaced the final five layers of the fine-tuned DenseNet with SNN, RVFL, and ELM components, respectively.

Main Results:

  • The DSNN model achieved the highest classification performance among the three proposed models.
  • Five-fold cross-validation demonstrated DSNN's accuracy (98.46% ± 2.05%), sensitivity (100.00% ± 0.00%), specificity (85.00% ± 20.00%), precision (98.36% ± 2.17%), and F1-score (99.16% ± 1.11%).
  • DSNN outperformed restricted DenseNet, spiking neural networks, and other state-of-the-art methods in comparative analyses.

Conclusions:

  • The DenseNet-based SNN (DSNN) model is highly effective for classifying brain diseases.
  • The proposed DSNN model offers a promising advancement in the accurate and efficient diagnosis of neurological conditions.
  • This research provides a robust deep learning approach for addressing the challenges in brain disease classification.