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Breaking down the Hierarchy: A New Approach to Leukemia Classification

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Abstract

The complexities inherent to leukemia, multifaceted cancer affecting white blood cells, pose considerable diagnostic and treatment challenges, primarily due to reliance on laborious morphological analyses and expert judgment that are susceptible to errors. Addressing these challenges, this study presents a refined, comprehensive strategy leveraging advanced deep-learning techniques for the classification of leukemia subtypes. We commence by developing a hierarchical label taxonomy, paving the way for differentiating between various subtypes of leukemia. The research further introduces a novel hierarchical approach inspired by clinical procedures capable of accurately classifying diverse types of leukemia alongside reactive and healthy cells. An integral part of this study involves a meticulous examination of the performance of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) as classifiers. The proposed method exhibits an impressive success rate, achieving approximately 90% accuracy across all leukemia subtypes, as substantiated by our experimental results. A visual representation of the experimental findings is provided to enhance the model’s explainability and aid in understanding the classification process.

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Author information

Authors and Affiliations

  1. Mohamed Bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE

    Ibraheem Hamdi, Hosam El-Gendy, Ahmed Sharshar, Mohamed Saeed, Muhammad Ridzuan & Mohammad Yaqub

  2. Sheikh Shakhbout Medical City, Abu Dhabi, UAE

    Shahrukh K. Hashmi, Naveed Syed, Imran Mirza, Shakir Hussain & Amira Mahmoud Abdalla

Authors
  1. Ibraheem Hamdi

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  2. Hosam El-Gendy

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  3. Ahmed Sharshar

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  4. Mohamed Saeed

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  5. Muhammad Ridzuan

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  6. Shahrukh K. Hashmi

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  7. Naveed Syed

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  8. Imran Mirza

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  9. Shakir Hussain

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  10. Amira Mahmoud Abdalla

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  11. Mohammad Yaqub

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Corresponding author

Correspondence toIbraheem Hamdi.

Editor information

Editors and Affiliations

  1. University of Pittsburgh, Pittsburgh, PA, USA

    Shandong Wu

  2. National Institute of Biomedical Imaging and Bioengineering, Bethesda, MD, USA

    Behrouz Shabestari

  3. Stanford University, Stanford, CA, USA

    Lei Xing

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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Hamdi, I.et al. (2024). Breaking down the Hierarchy: A New Approach to Leukemia Classification. In: Wu, S., Shabestari, B., Xing, L. (eds) Applications of Medical Artificial Intelligence. AMAI 2023. Lecture Notes in Computer Science, vol 14313. Springer, Cham. https://doi.org/10.1007/978-3-031-47076-9_11

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JPY 3498
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JPY 11439
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  • Available as EPUB and PDF
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JPY 14299
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  • Compact, lightweight edition
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