Classification of White Blood Cell Abnormalities for Early Detection of Myeloproliferative Neoplasms Syndrome Based on K-Nearest Neighborr
Fitri Zilvanhisna Emka
Primary Author
mixed material
bibliography
Semarang
Universitas Negeri Semarang
2020
e
English
hlm : 136-142
Scientific Journal of Informatics
Abstract
The myeloproliferative neoplasms (MPNs) are clonal hematopoietic stem cell disorders characterized by dysregulated proliferation and expansion of one or more of the myeloid lineages. The initial symptoms of MPN is a bone marrow abnormalities when producing red blood cells, white blood cells and platelets in large numbers and uncontrolled. An automatic and accurate white blood cell abnormality classification system is needed. This research uses digital image processing techniques such as conversion to the modified CIELab color space, segmentation techniques based on threshold values and feature extraction processes that produce four morphological features consisting of area, perimeter, metric and compactness. then the four features become input to the K-Nearest Neighborr (KNN) method. The testing process is based on variations in the value of K to get the best accuracy percentage of 94.3% tested on 159 test data.
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SJI
24077658
Perpustakaan Teknik UPI YAI
SJI V7N1 MAY 2020
SJI1-013
Perpustakaan FT UPI YAI
SJI V7N1 MAY 2020
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2023-02-07 14:43:53
2023-02-07 14:43:53
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