ArticleMedical & biological engineering & computing2014
Feature selection and classification of leukocytes using random forest.
Article in Medical & biological engineering & computing, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- GA-SMOTE-RF Enhanced Kalman Filter with Adaptive Noise Reduction.Sensors (Basel, Switzerland) · 2026Article
- Artificial Intelligence and Data Science Methods for Automatic Detection of White Blood Cells in Images.Journal of imaging informatics in medicine · 2026Review
- Multiple Strategies Confirm the Anti Hepatocellular Carcinoma Effect of Cinnamic Acid Based on the PI3k-AKT Pathway.Pharmaceuticals (Basel, Switzerland) · 2025Article
- A new feature selection approach with binary exponential henry gas solubility optimization and hybrid data transformation methods.MethodsX · 2024Review
- Estimating Health-Related Quality of Life Based on Demographic Characteristics, Questionnaires, Gait Ability, and Physical Fitness in Korean Elderly Adults.International journal of environmental research and public health · 2021Article
- Drug-induced cell viability prediction from LINCS-L1000 through WRFEN-XGBoost algorithm.BMC bioinformatics · 2021Article
- Combining DC-GAN with ResNet for blood cell image classification.Medical & biological engineering & computing · 2020Article
- RF-PCA: A New Solution for Rapid Identification of Breast Cancer Categorical Data Based on Attribute Selection and Feature Extraction.Frontiers in genetics · 2020Article
- A Novel Mechanical Fault Feature Selection and Diagnosis Approach for High-Voltage Circuit Breakers Using Features Extracted without Signal Processing.Sensors (Basel, Switzerland) · 2019Article
- Computer-Aided Diagnosis of Acute Lymphoblastic Leukaemia.Computational and mathematical methods in medicine · 2018Review
- Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships.Medical & biological engineering & computing · 2017Article
- Effect of fuzzy partitioning in Crohn's disease classification: a neuro-fuzzy-based approach.Medical & biological engineering & computing · 2017Article
- Predicting the Types of Ion Channel-Targeted Conotoxins Based on AVC-SVM Model.BioMed research international · 2017Article
Corrections and comments
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Authors and funding
2 authors.
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Abstract
In automatic segmentation of leukocytes from the complex morphological background of tissue section images, a vast number of artifacts/noise are also extracted causing large amount of multivariate data generation. This multivariate data degrades the performance of a classifier to discriminate between leukocytes and artifacts/noise. However, the selection of prominent features plays an important role in reducing the computational complexity and increasing the performance of the classifier as compared to a high-dimensional features space. Therefore, this paper introduces a novel Gini importance-based binary random forest feature selection method. Moreover, the random forest classifier is used to classify the extracted objects into artifacts, mononuclear cells, and polymorphonuclear cells. The experimental results establish that the proposed method effectively eliminates the irrelevant features, maintaining the high classification accuracy as compared to other feature reduction methods.
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Registered trials
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