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Related Concept Videos

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Spectral-aware CNN with learnable biorthogonal units and depthwise convolutions for multi-class blood cell

Sannasi Chakravarthy Sr1, Harikumar Rajaguru1, Rajesh Kumar Dhanaraj2

  • 1Department of Electronics and Communicaiton Engineering, Bannari Amman Institute of Technology, Sathyamangalam 638 401, India.

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Summary

This study introduces a novel deep learning model for accurate blood cell classification, achieving 99.18% accuracy. The model enhances early disease diagnosis like leukemia and anemia by improving feature retention and reducing computational costs.

Keywords:
Blood cell classificationDeep learningDepthwise separable convolutionSpectral-aware downsamplingWavelet CNN

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Area of Science:

  • Medical Imaging
  • Computational Biology
  • Artificial Intelligence

Background:

  • Accurate peripheral blood cell classification is crucial for early diagnosis of diseases like leukemia and anemia.
  • Existing methods may face challenges in feature retention and computational efficiency.

Purpose of the Study:

  • To propose a novel hybrid deep learning model for multi-class blood cell classification.
  • To enhance the accuracy and efficiency of blood cell classification for improved disease diagnosis.

Main Methods:

  • Developed a Spectral-Aware Convolutional Neural Network (CNN) model incorporating Learnable Spectral Biorthogonal Downsampling Units (LSBDUs).
  • Replaced conventional pooling layers with wavelet-inspired LSBDUs for superior feature preservation.
  • Integrated Depthwise Separable Convolutions to reduce computational overhead and training costs.
  • Utilized a balanced dataset of 17,092 images across eight blood cell classes, employing stratified data splitting, advanced augmentation, and label smoothing.

Main Results:

  • Achieved an overall classification accuracy of 99.18% on the blood cell dataset.
  • Demonstrated superior class-wise performance compared to existing methods.
  • Showcased improved generalization across all classes without overfitting, validating the model's robustness.

Conclusions:

  • The proposed Spectral-Aware CNN model with LSBDUs and depthwise separable convolutions offers a highly accurate and efficient solution for multi-class blood cell classification.
  • This advancement holds significant potential for improving early disease detection and diagnosis in clinical settings.
  • The model's design effectively balances feature preservation with reduced computational complexity.