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All Journal Infolitika Journal of Data Science
Edrian Hadinata
Department of Information System, Universitas Harapan Medan, Medan 20216, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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An Implementation of Hybrid CNN-XGBoost Method for Leukemia Detection Problem Taufiq Hidayat; Edrian Hadinata; Irfan Sudahri Damanik; Zakial Vikki; Irvanizam Irvanizam
Infolitika Journal of Data Science Vol. 1 No. 1 (2023): September 2023
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v1i1.87

Abstract

Leukemia is a blood cancer in which blood cells become malignant and uncontrolled. It can cause damage to the function of the body's organs. Several machine learning methods have been used to automatically detect biomedical images, including blood cell images. In this study, we utilized a hybrid machine learning method, called a hybrid Convolutional Neural Network-eXtreme Gradient Boosting (CNN-XGBoost) method to detect leukemia in blood cells. The hybrid method combines two machine learning methods. We use CNN as the basic classifier and XGBoost as the main classification method. The aim of this methodology was to assess whether incorporating the basic classification method would lead to an enhancement in the performance of the main classification model. The experimental findings demonstrated that the utilization of XGBoost as the main classifier led to a marginal increase in accuracy, elevating it from 85.32% to 85.43% compared to the basic CNN classification. This research highlights the potential of hybrid machine learning approaches in biomedical image analysis and their role in advancing the early diagnosis of leukemia and potentially other medical conditions.
Co-Authors Irfan Sudahri Damanik Irvanizam Zamanhuri Taufiq Hidayat Zakial Vikki