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CoTCoNet: An optimized coupled transformer-convolutional network with an adaptive graph reconstruction for leukemia

Chandravardhan Singh Raghaw1, Arnav Sharma2, Shubhi Bansal1

  • 1Department of Computer Science and Engineering, Indian Institute of Technology Indore, Khandwa Road, Simrol, Indore, 453552, Madhya Pradesh, India.

Computers in Biology and Medicine
|July 7, 2024
PubMed
Summary

A new Coupled Transformer Convolutional Network (CoTCoNet) framework significantly improves leukemia classification accuracy. This AI model enhances blood smear analysis, offering a more efficient and reliable diagnostic tool for hematological malignancies.

Keywords:
Acute lymphoblastic leukemiaCell classificationConvolutional neural networksDeep learningTransformer

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Area of Science:

  • Hematology
  • Computational Biology
  • Medical Imaging

Background:

  • Accurate blood smear analysis is vital for diagnosing leukemia and hematological malignancies.
  • Manual methods are time-consuming, error-prone, and struggle with cell differentiation.
  • Existing image processing methods face challenges distinguishing benign from malignant cell morphology.

Purpose of the Study:

  • To develop an advanced computational framework for automated leukemia classification.
  • To overcome limitations of manual analysis and conventional image processing techniques.
  • To improve accuracy and efficiency in diagnosing hematological malignancies.

Main Methods:

  • Proposed the Coupled Transformer Convolutional Network (CoTCoNet) framework.
  • Integrated dual-feature extraction for global and spatial patterns.
  • Employed a graph-based module, meta-heuristic optimization, and leukocyte segmentation/synthesis.

Main Results:

  • Achieved high accuracy (0.9894) and F1-Score (0.9893) on a large dataset (16,982 cells).
  • Demonstrated superior performance over state-of-the-art methods on diverse datasets.
  • Validated generalizability across multiple public datasets.

Conclusions:

  • CoTCoNet offers a significant advancement in leukemia classification accuracy and efficiency.
  • The framework provides explainable visualizations, aiding clinical interpretation.
  • CoTCoNet shows strong potential for clinical application in hematological diagnostics.