Detection of Essential Thrombocythemia based on Platelet Count using
Channel Area Thresholding

Detection of Essential Thrombocythemia based on Platelet Count using
Channel Area Thresholding

abnormalities, platelet count, essential thrombocythemia, channel area thresholding, k-nearest neighbor.

Essential Thrombocythemia is one of the Myeloproliferative Neoplasms Syndrome where the mutation of the JAK2V617F gene
causes the bone marrow to produce excessive platelets. For early detection of Essential Thrombocythemia disease using a full
blood count and peripheral blood smear examination. The main characteristic is that giant platelets are found as large as
young lymphocytes with a number of more than 21 cells in one field of view. The purpose of this research is to detect Essential
Thrombocythemia by counting the number of platelets in the peripheral blood smear image. This research utilizes computer
vision technique where the research stages consist of peripheral blood smear image, color conversion, image enhancement,
segmentation, labeling process, feature extraction and K-Nearest Neighbor classification. There are three features used,
namely the number of platelet cells, area and perimeter. The K-Nearest Neighbor method is able to classify 215 training data
with an accuracy of 98.13% and classify 40 testing data with an accuracy of 100% based on the value of K = 3

Prawidya Destarianto1
, Ainun Nurkharima Noviana2
, Zilvanhisna Emka Fitri3
, Arizal Mujibtamala Nanda Imron4

Politeknik Negeri Jember

1 februari 2022

Fajar bagus W

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Indonesia

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