Open AccessDetection of Essential Thrombocythemia based on Platelet Count using Channel Area Thresholding
Author(s) -
Prawidya Destarianto,
Ainun Nurkharima Noviana,
Zilvanhisna Emka Fitri,
Arizal Mujibtamala Nanda Imron
Publication year - 2022
Publication title -
jurnal resti (rekayasa sistem dan teknologi informasi)
Language(s) - English
Resource type - Journals
ISSN - 2580-0760
DOI - 10.29207/resti.v6i1.3571
Subject(s) - essential thrombocythemia , thresholding , artificial intelligence , platelet , bone marrow , feature (linguistics) , feature extraction , peripheral blood , pattern recognition (psychology) , medicine , pathology , segmentation , computer science , image (mathematics) , linguistics , philosophy
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.