Enhanced CT image classification for kidney stones using pruned ConvNeXt and two-tier optimization

ABSTRACT Background In this work, an innovative two-tier feature-optimized deep learning model is proposed for kidney stone detection in CT images. The framework uses a new modified ConvNeXt learning model to capture complex image patterns from CT images, along with the use of a LightGBM classifier. Methods A two-tier optimization strategy is implemented to refine the detection process. Initially, Dynamic Channel Pruning is used within the ConvNeXt architecture to identify and retain the most informative channels during feature extraction. By dynamically evaluating the importance of each channel, this step processes the most relevant channels, reducing computational complexity and highlighting critical features. Then, the Pufferfish Optimization Algorithm (POA) is applied for optimal feature selection. This optimization helps isolate the most discriminative features for kidney stone detection. Additionally, POA is applied to adjust the hyperparameters of the LightGBM classifier to increase classification accuracy and efficiency. Results The proposed model achieves the highest accuracy of 97.8%, compared with other models. Conclusion The proposed model achieves enhanced detection accuracy and efficiency through the use of Dynamic Channel Pruning and Pufferfish Optimization. This model offers a promising solution for kidney stone detection in CT images.

• Publication year

  2025

• Venue

  Acta Clinica Belgica

• Publication date

  2025-11-02

• Fields of study

  Medicine, Computer Science, Engineering

• Identifiers
  DOI 10.1080/17843286.2025.2586626PMID 41217965
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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