Evaluating Thresholding-Based Segmentation and Humoment Feature Extraction in Acute Lymphoblastic Leukemia Classification using Gaussian Naive Bayes

Nurul Rismayanti; Ahmad Naswin; Umar Zaky; Muhammad Zakariyah; Dwi Amalia Purnamasari

doi:10.56705/ijaimi.v1i2.99

Nurul Rismayanti Universitas Muslim Indonesia
Ahmad Naswin Universitas Megarezky Makassar
Umar Zaky Universitas Teknologi Yogyakarta
Muhammad Zakariyah Universitas Teknologi Yogyakarta
Dwi Amalia Purnamasari Politeknik Negeri Batam

DOI: https://doi.org/10.56705/ijaimi.v1i2.99

Keywords: Acute Lymphoblastic Leukemia, Gaussian Naive Bayes, Hu Moment Feature Extraction, Image Segmentation, Thresholding, Machine Learning, Medical Image Analysis

Abstract

This study, titled "Evaluating Thresholding-Based Segmentation and HuMoment Feature Extraction in Acute Lymphoblastic Leukemia Classification using Gaussian Naive Bayes," investigates the application of image processing and machine learning techniques in the classification of Acute Lymphoblastic Leukemia (ALL). Utilizing a dataset of microscopic blood smear images, the research focuses on the efficacy of thresholding-based segmentation and Hu moment feature extraction in distinguishing between benign and malignant cases of ALL. Gaussian Naive Bayes, known for its simplicity and effectiveness, is employed as the classification algorithm. The study adopts a 5-fold cross-validation approach to evaluate the model's performance, with particular emphasis on metrics such as accuracy, precision, recall, and F1-score. Results indicate a high precision rate across all folds, averaging approximately 84.13%, while exhibiting variability in accuracy, recall, and F1-scores. These findings suggest that while the model is effective in identifying malignant cases, further refinements are necessary for improving overall accuracy and consistency. This research contributes to the field of medical image analysis by demonstrating the potential of combining simple yet efficient techniques for the automated diagnosis of hepatological diseases. It highlights the importance of integrating image processing with machine learning to enhance diagnostic accuracy in medical applications.

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