Yessi Jusman, Slamet Riyadi, Amir Faisal, Siti Nurul Aqmariah Mohd Kanafiah, Zeehaida Mohamed, Rosline Hassan
Leukemia is cancer that attacks the tissues of white blood cells. It occurs when the body produces abnormal blood cells exceeding normal limits; thus, causing them not to function properly. It has a huge effect on the immune system of humans. Medical personnel currently need a long time to recognize leukemia and distinguish acute leukemia cells from normal cells. This study aims to build a classification system of white blood cell images using a feature extraction technique with Hu moment invariants and Support Vector Machine (SVM) classification methods. In this study, the data of 800 blood image samples were divided into two classes, acute and normal, with each class having 400 sample images. The calculation of the average accuracy and average time value on the system obtained the accuracy value of 88% and the required time of 3.73 seconds. The highest accuracy values for the testing data is 95% with duration time 0.89 seconds. The system could classify the leukemia images using Hu moment invariants and SVM. © 2021 IEEE.
Universitas Muhamadiyah Yogyakarta, Faculty of Engineering, Department of Electrical Engineering, Yogyakarta, Indonesia; Institut Teknologi Sumatera, Department of Biomedical Engineering, Lampung, Indonesia; Universiti Malaysia, School of Mechatronics Engineering, Perlis, Malaysia; Universiti Sains Malaysia, Department of Microbiology and Parasitology, Kelantan, Malaysia; Universiti Sains Malaysia, Department of Haematology, Kelantan, Malaysia
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