SVM Model for Blood Cell Classification using Interpretable Features Outperforms CNN Based Approaches William Franz Lamberti 1 George Mason University June 4, 2020 1 MS Statistical Science PhD Candidate Computational Sciences and Informatics William Franz Lamberti 1
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SVM Model for Blood Cell Classification using Interpretable ......CNN approaches from: Mohammad Mahmudul Alam, and Mohammad Tariqul Islam. \Machine Learning Approach of Automatic Identi
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SVM Model for Blood Cell Classification usingInterpretable Features Outperforms CNN Based
Approaches
William Franz Lamberti 1
George Mason University
June 4, 2020
1MS Statistical SciencePhD Candidate Computational Sciences and Informatics
William Franz Lamberti 1
Outline
IntroductionBlood Cell ClassificationCNNs
Goal
Data
ModelMetricsAlgorithmModel: Algorithm
ResultsConfusion TableClassification Rates
Conclusion
Acknowledgements
William Franz Lamberti 2
Introduction: Blood Cell Classification
▸ Important task inHealth Sciences
▸ Counts are used tomeasure overallhealth of patient
▸ Often done by hand,which is tedious
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Introduction: CNNs
▸ Convolution NeuralNetworks (CNNs)very popular methodin Computer Vision
▸ Lots of differentapplications and verypowerful
▸ Difficult to interpretand explain
▸ Require a largeamount of data
Image from: Redmon, Joseph et al. “You Only Look Once: Unified, Real-TimeObject Detection.” arXiv.org (2016): n. pag. Web.
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Goal
▸ Build model that outperforms state of the art in classifyingobjects
▸ Use interpretable metrics
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Data
▸ Publicly available BBCDdataset:https://github.com/
Shenggan/BCCD_Dataset
▸ Classes▸ Red Blood Cells (RBCs):
4153▸ White Blood Cells
(WBCs): 372▸ Platelets:361
▸ Objects are extracted withgiven annotation file anduniversal segmentationoperators are applied
CNN approaches from: Mohammad Mahmudul Alam, and Mohammad TariqulIslam. “Machine Learning Approach of Automatic Identification and Countingof Blood Cells.” Healthcare Technology Letters 6.4 (2019)
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Conclusion
▸ SVM outperforms all other approaches▸ Overall Mean Outperformance: 5%▸ Underperforms for WBC
▸ Future Work▸ Develop segmentation technique without need for annotations▸ Improve classification▸ Develop for other applications such as COVID-19
▸ Code and Manuscript available▸ Code: https://github.com/billyl320/bccd_svm▸ Manuscript: https://github.com/billyl320/bccd_svm/