ArticleScientific reports2021
New segmentation and feature extraction algorithm for classification of white blood cells in peripheral smear images.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- A universal visual foundation model for computational cytopathology.Nature cancer · 2026Article
- An AI-enabled tool for quantifying overlapping red blood cell sickling dynamics in microfluidic assays.Lab on a chip · 2026Article
- A feasibility study on multimodal CT-MRI registration using segmentation aid and CoLlAGe feature extraction approach.Abdominal radiology (New York) · 2026Article
- Hough's Transform-Based IoT Device for Automated Identification and Prediction of Blood Groups.Journal of biomedical physics & engineering · 2026Article
- S2S: A deep learning method for the radiological diagnosis of fine-grained diseases spanning screening to subtyping.Patterns (New York, N.Y.) · 2025Article
- A dual-branch deep learning network for circulating tumor cells classification.Journal of translational medicine · 2025Article
- Test-Time Augmentation for Cross-Domain Leukocyte Classification via OOD Filtering and Self-Ensembling.Journal of imaging · 2025Article
- Domain knowledge-infused pre-trained deep learning models for efficient white blood cell classification.Scientific reports · 2025Article
- Deep generative classification of blood cell morphology.Nature machine intelligence · 2025Article
- Research advances in the adjunctive diagnosis of acute myeloid leukemia.Frontiers in oncology · 2025Review
- Improving the generalizability of white blood cell classification with few-shot domain adaptation.Journal of pathology informatics · 2024Article
- A hybrid approach based on multipath Swin transformer and ConvMixer for white blood cells classification.Health information science and systems · 2024Article
- Engineered feature embeddings meet deep learning: A novel strategy to improve bone marrow cell classification and model transparency.Journal of pathology informatics · 2024Article
- Adversarial training collaborating hybrid convolution-transformer network for automatic identification of reactive lymphocytes in peripheral blood.Biomedical optics express · 2024Article
- Cell Nuclear Segmentation of B-ALL Images Based on MSFF-SegNeXt.Journal of multidisciplinary healthcare · 2024Article
- Article
- Microscopic parasite malaria classification using best feature selection based on generalized normal distribution optimization.PeerJ. Computer science · 2024Article
- High-accuracy morphological identification of bone marrow cells using deep learning-based Morphogo system.Scientific reports · 2023Article
- An Explainable Vision Transformer Model Based White Blood Cells Classification and Localization.Diagnostics (Basel, Switzerland) · 2023Article
- Explainable CAD System for Classification of Acute Lymphoblastic Leukemia Based on a Robust White Blood Cell Segmentation.Cancers · 2023Article
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4 authors.
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Abstract
This article addresses a new method for the classification of white blood cells (WBCs) using image processing techniques and machine learning methods. The proposed method consists of three steps: detecting the nucleus and cytoplasm, extracting features, and classification. At first, a new algorithm is designed to segment the nucleus. For the cytoplasm to be detected, only a part of it located inside the convex hull of the nucleus is involved in the process. This attitude helps us overcome the difficulties of segmenting the cytoplasm. In the second phase, three shapes and four novel color features are devised and extracted. Finally, by using an SVM model, the WBCs are classified. The segmentation algorithm can detect the nucleus with a dice similarity coefficient of 0.9675. The proposed method can categorize WBCs in Raabin-WBC, LISC, and BCCD datasets with accuracies of 94.65%, 92.21%, and 94.20%, respectively. Besides, we show that the proposed method possesses more generalization power than pre-trained CNN models. It is worth mentioning that the hyperparameters of the classifier are fixed only with the Raabin-WBC dataset, and these parameters are not readjusted for LISC and BCCD datasets.
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