A NEW COMPUTER-AIDED DIAGNOSIS OF PRECISE MALARIA PARASITE DETECTION IN MICROSCOPIC IMAGES USING A DECISION TREE MODEL WITH SELECTIVE OPTIMAL FEATURES

Author:

Phumkuea Thanakorn1ORCID,Nilvisut Phurich2,Wongsirichot Thakerng2,Damkliang Kasikrit2

Affiliation:

1. College of Digital Science, Prince of Songkla University Songkhla, Thailand

2. Division of Computational Science Faculty of Science, Prince of Songkla University Songkhla, Thailand

Abstract

Malaria is a life-threatening mosquito-borne disease. Recently, the number of malaria cases has increased worldwide, threatening vulnerable populations. Malaria is responsible for a high rate of morbidity and mortality in people all around the world. Each year, many people, die from this disease, according to the World Health Organization (WHO). Thick and thin blood smears are used to determine parasite habitation and computer-aided diagnosis (CADx) techniques using machine learning (ML) are being used to assist. CADx reduces traditional diagnosis time, lessens socio-economic impact, and improves quality of life. This study develops a simplified model with selective features to reduce processing power and further shorten diagnostic time, which is important to resource-constrained areas. To improve overall classification results, we use a decision tree (DT)-based approach with image pre-processing called optimal features to identify optimal features. Various feature selection and extraction techniques are used, including information gain (IG). Our proposed model is compared to a benchmark state-of-art classification model. For an unseen dataset, our proposed model achieves accuracy, precision, recall, F-score, and processing time of 0.956, 0.949, 0.964, 0.956, and 9.877 s, respectively. Furthermore, our proposed model’s training time is less than those of the state-of-the-art classification model, while the performance metrics are comparable.

Funder

The National Science, Research, and Innovation Fund

Publisher

National Taiwan University

Subject

Biomedical Engineering,Bioengineering,Biophysics

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Recent Advancements in Detection and Quantification of Malaria Using Artificial Intelligence;UMYU Journal of Microbiology Research (UJMR);2024-09-12

2. Computer-Aided Diagnosis Systems for Automatic Malaria Parasite Detection and Classification: A Systematic Review;Electronics;2024-08-11

3. A Multilayer Perceptron Based Mobile Diagnosis System for Malaria Fever;2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG);2024-04-02

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