ArticlePloS one2019
Deep learning approach to peripheral leukocyte recognition.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- Clinical Validation of the MC-80 Digital Morphology Analyzer in Leukocyte Classification: A Large-Scale Comparison With Manual Microscopy.International journal of laboratory hematology · 2026Article
- Incorporating artificial intelligence into morphological diagnosis of acute leukemias: Current landscape, challenges and prospects (Review).Oncology reports · 2026Review
- Deep Learning Identifies Abnormal Promyelocytes in Peripheral Blood Based on Morphological Analysis.Diagnostics (Basel, Switzerland) · 2026Article
- Article
- Spectral-aware CNN with learnable biorthogonal units and depthwise convolutions for multi-class blood cell classification.MethodsX · 2025Article
- Deep learning-based object detection algorithms in medical imaging: Systematic review.Heliyon · 2025Review
- An efficient low-shot class-agnostic counting framework with hybrid encoder and iterative exemplar feature learning.PloS one · 2025Article
- An Innovative Hybrid Model for Automatic Detection of White Blood Cells in Clinical Laboratories.Diagnostics (Basel, Switzerland) · 2024Article
- Application of image recognition technology in pathological diagnosis of blood smears.Clinical and experimental medicine · 2024Review
- Enhancing parasitic organism detection in microscopy images through deep learning and fine-tuned optimizer.Scientific reports · 2024Article
- Improved YOLOv7 Algorithm for Detecting Bone Marrow Cells.Sensors (Basel, Switzerland) · 2023Article
- Deep Learning-Based Precision Analysis for Acrosome Reaction by Modification of Plasma Membrane in Boar Sperm.Animals : an open access journal from MDPI · 2023Article
- An Explainable Vision Transformer Model Based White Blood Cells Classification and Localization.Diagnostics (Basel, Switzerland) · 2023Article
- Automatic identification of medically important mosquitoes using embedded learning approach-based image-retrieval system.Scientific reports · 2023Article
- Applications of Artificial Intelligence in Philadelphia-Negative Myeloproliferative Neoplasms.Diagnostics (Basel, Switzerland) · 2023Review
- A deep learning model for detection of leukocytes under various interference factors.Scientific reports · 2023Article
- White Blood Cells Classification Using Entropy-Controlled Deep Features Optimization.Diagnostics (Basel, Switzerland) · 2023Article
- Using deep DenseNet with cyclical learning rate to classify leukocytes for leukemia identification.Frontiers in oncology · 2023Article
- Common statistical concepts in the supervised Machine Learning arena.Frontiers in oncology · 2023Review
- Automatic identifying and counting blood cells in smear images by using single shot detector and Taguchi method.BMC bioinformatics · 2022Article
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Authors and funding
6 authors.
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Abstract
Microscopic examination of peripheral blood plays an important role in the field of diagnosis and control of major diseases. Peripheral leukocyte recognition by manual requires medical technicians to observe blood smears through light microscopy, using their experience and expertise to discriminate and analyze different cells, which is time-consuming, labor-intensive and subjective. The traditional systems based on feature engineering often need to ensure successful segmentation and then manually extract certain quantitative and qualitative features for recognition but still remaining a limitation of poor robustness. The classification pipeline based on convolutional neural network is of automatic feature extraction and free of segmentation but hard to deal with multiple object recognition. In this paper, we take leukocyte recognition as object detection task and apply two remarkable object detection approaches, Single Shot Multibox Detector and An Incremental Improvement Version of You Only Look Once. To improve recognition performance, some key factors involving these object detection approaches are explored and the detection models are generated using the train set of 14,700 annotated images. Finally, we evaluate these detection models on test sets consisting of 1,120 annotated images and 7,868 labeled single object images corresponding to 11 categories of peripheral leukocytes, respectively. A best mean average precision of 93.10% and mean accuracy of 90.09% are achieved while the inference time is 53 ms per image on a NVIDIA GTX1080Ti GPU.
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