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Bhima Caraka
Universitas Gadjah Mada
Electrical & Electronics Engineering

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Klasifikasi Sel Darah Putih Menggunakan Metode Support Vector Machine (SVM) Berbasis Pengolahan Citra Digital Bhima Caraka; Bakhtiar Alldino Ardi Sumbodo; Ika Candradewi
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 7, No 1 (2017): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (658.547 KB) | DOI: 10.22146/ijeis.15420

Abstract

White blood cells are classified into five types (basophils, eosinophils, neutrophils, lymphocytes and monocytes) with additional classes lymphoblast cells from microscope images are processed. By applying image processing, image its white blood cells extracted using the Histogram Oriented Gradient. Feature extraction results obtained then classified using Support Vector Machine method by comparing the results of two different kernel parameters: kernel Linear and kernel Radial Basis Function (RBF). Classification evaluated with these parameters: Accuracy, specificity, and sensitivity.Obtained an accuracy of 72.26% from the detection of white blood cells in the microscope image. The average value of microscope images of patients and different kernel every white blood cells (monocytes, basophils, neutrophils, eosinophils, lymphocytes and lymphoblast) were evaluated with these parameters. Results of the study show the classification system has an average value of 82.20% accuracy (RBF Patient 1), 81.63% (RBF Patient 2) and 78.73% (Linear Patient 1), 79.55% (Linear Patient 2 ), then the value of specificity of 89.91% (RBF patient 1), 92.18% (RBF patient 2) and 88.06% (Linear patient 1), 91.34% (Linear patient 2), and sensitivity values 15 , 45% (RBF patient 1), 12.97% (RBF patient 2) and 13.33% (Linear patient 1), 12.50% (Linear patient 2).
Co-Authors Bakhtiar Alldino Ardi Sumbodo Ika Candradewi