A Comparative Study of Segmentation Method for Computer-aided Diagnosis (CAD) Leukemia AML Subtype M0, M1, and M2

A computer-based diagnosis model for Acute Myelogenous Leukemia (AML) is carried out using white blood cell image processing. The stages in computer-aided diagnosis (CAD) are included pre-processing, segmentation, feature extraction, and classification. The segmentation method has many approaches, namely, clustering, region growing, and thresholding. The number of approaches that can be used requires proper selection because it will have an impact on CAD performance. This study aims to conduct a comparative study of the performance of the WBC segmentation method on the AML M0, M1, and M2 subtype leukemia CAD system. The segmentation algorithm used is k-means, fuzzy c-means, SOM, watershed, chan vese (active contour), otsu thresholding, and histogram. The feature extraction method uses GLCM, while the classification algorithms tested are SVM, Random-forest, decision tree, naive Bayesian, and k-NN. The test results show that the histogram segmentation method is able to provide the best average performance when using SVM, namely 90.3% accuracy, 85.9% sensitivity, and 92.7% specificity.

@article{Wiharto2021,
  title     = {A Comparative Study of Segmentation Method for Computer-aided Diagnosis (CAD) Leukemia AML Subtype M0, M1, and M2},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {11},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Wiharto and Wisnu Widiarto and Esti Suryani and Nurmajid Hidayatullah},
  doi       = {10.14569/IJACSA.2021.0121167},
  url       = {https://doi.org/10.14569/IJACSA.2021.0121167}
}