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White blood cells (WBCs) play a crucial role in the immune system, with their morphology and subtype counts serving as key indicators for diagnosing conditions like anemia and leukemia. However, manual WBC classification in peripheral blood smears is time-consuming, highlighting the need for automated WBC classification systems. Recent advancements in deep learning, including convolutional neural networks and vision transformers, have demonstrated significant potential in medical imaging by effectively extracting meaningful features. This paper surveys state-of-the-art techniques, examining relevant datasets and WBC types. We conduct a comprehensive performance analysis of nine models on two benchmark datasets, BCCD and PBC. Our findings indicate that ConvNeXt achieves a weighted average accuracy (WAA) of 89.58% and an F1-Score of 90.00% on the BCCD dataset, while DenseNet demonstrates superior performance on the PBC dataset, with a WAA of 98.88% and an F1-Score of 98.88%.

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A Brief Review of State-of-the-Art Classification Methods on Benchmark Peripheral Blood Smears Datasets

  • 会议论文

  • 发表日期：

    2026年

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  • Muhammad Suhaib Kanroo,
  • Hadia Showkat Kawoosa,
  • Tanushri,
  • Medha Aggarwal,
  • Puneet Goyal

摘要

White blood cells (WBCs) play a crucial role in the immune system, with their morphology and subtype counts serving as key indicators for diagnosing conditions like anemia and leukemia. However, manual WBC classification in peripheral blood smears is time-consuming, highlighting the need for automated WBC classification systems. Recent advancements in deep learning, including convolutional neural networks and vision transformers, have demonstrated significant potential in medical imaging by effectively extracting meaningful features. This paper surveys state-of-the-art techniques, examining relevant datasets and WBC types. We conduct a comprehensive performance analysis of nine models on two benchmark datasets, BCCD and PBC. Our findings indicate that ConvNeXt achieves a weighted average accuracy (WAA) of 89.58% and an F1-Score of 90.00% on the BCCD dataset, while DenseNet demonstrates superior performance on the PBC dataset, with a WAA of 98.88% and an F1-Score of 98.88%.