Multiclass Blood Cell Classification Using Contourlet Transform and Metaheuristic-Optimized Deep Features with Clustering-Based Decision Making

Authors

  • Omid Eslamifar * Department of Electrical Engineering, Saveh Branch, Islamic Azad University, Saveh, Iran.
  • Mohammadreza Soltani Department of Electrical Engineering, Khomeinishahr Branch, Islamic Azad University, Khomeinishahr, Isfahan, Iran.
  • Seyed Mohammad Jalal Rastegar Fatemi Department of Electrical Engineering, Saveh Branch, Islamic Azad University, Saveh, Iran.

https://doi.org/10.22105/scfa.v1i4.58

Abstract

Analysis by pathologists is time-consuming and error-prone due to the similarity among cell types. To address this, we propose a hybrid method combining deep learning and contourlet transform for multiclass blood cell classification. Features are optimized using a metaheuristic algorithm inspired by African vultures. Experimental results on the Jiangxi Tecom dataset demonstrate high performance, achieving classification accuracies of up to 97% for specific cell types. This approach improves diagnostic reliability by leveraging feature-level fusion and clustering-based decision making.

Keywords:

White blood cell, Classification, Contourlet transform, Recurrent neural network, Precision

References

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Published

2024-10-14

Issue

Vol. 1 No. 4 (2024): Soft Computing Fusion with Applications

Section

Articles

How to Cite

Multiclass Blood Cell Classification Using Contourlet Transform and Metaheuristic-Optimized Deep Features with Clustering-Based Decision Making. (2024). Soft Computing Fusion With Applications , 1(4), 186-198. https://doi.org/10.22105/scfa.v1i4.58

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