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In medical practice, morphological analysis presents multifactorial drawbacks mainly related to the quality of the sample and the skill of the human expert. In this regard, artificial vision offers an alternative for the recognition of biological tissues as a support in medical diagnosis. For this reason, in this work we evaluate different architectures of simple convolutional networks for the classification of five types of acute myelocytic leukemia, which have been scarcely tested with these techniques. The results show that the depth of the models is not related to better performance.

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Classification of Myelocytic Leukemia by Simple Convolutional Neural Networks with Structural Variants

  • 会议论文

  • 发表日期：

    2025年

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  • Rocio Ochoa-Montiel,
  • C. Sánchez-López,
  • Mauricio Hernández-Romano

摘要

In medical practice, morphological analysis presents multifactorial drawbacks mainly related to the quality of the sample and the skill of the human expert. In this regard, artificial vision offers an alternative for the recognition of biological tissues as a support in medical diagnosis. For this reason, in this work we evaluate different architectures of simple convolutional networks for the classification of five types of acute myelocytic leukemia, which have been scarcely tested with these techniques. The results show that the depth of the models is not related to better performance.