[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124064-en":3,"doc-seo-124064-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124064,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine Learning-based Detection of Malaria Infection through Blood Sample Analysis - Research focus","Malaria is a life-threatening disease caused by Plasmodium parasites transmitted through infected mosquitoes, making early and accurate detection essential for timely treatment and control. The document proposes an automated malaria detection system that supports rapid testing and can be deployed in portable diagnostic devices for remote or resource-limited settings. By replacing microscope-based manual blood smear examination, the approach reduces time, labor, and expertise dependency while limiting false results. It further supports epidemiological monitoring by tracking prevalence and spread. The work applies machine learning with image processing to achieve high accuracy and efficiency in identifying malaria infection from blood samples.","Journal of Science and Technology  \nISSN: 2456-5660 Volume 8, Issue 12 (Dec-2023)  \n [www.jst.org.in](www.jst.org.in) DOI:[https://doi.org/10.46243/jst.2023.v8.i12.pp185-198](https://doi.org/10.46243/jst.2023.v8.i12.pp185-198)   \n[Machine Learning-based Detection of Malaria Infection through Blood](Machine Learning-based Detection of Malaria Infection through Blood)  \nSample Analysis  \nN. Teja 1, K. Vyshnavi2, M. Pavana Sri2, K. Bharathi2 1Assistant Professor,2UG Students, Department of Information Technology  \n1,2Malla Reddy Engineering College for Women, Maisammaguda, Dhulapally, Kompally,  \nSecunderabad-500100, Telangana, India.  \nTo Cite this Article  \nN. Teja, K. Vyshnavi, M. Pavana Sri, K. Bharathi,“Machine Learning-based Detection of Malaria Infection through Blood Sample Analysis ” Journal of Science and Technology, Vol. 08, Issue 12-Dec 2023, pp185-194  \nArticle Info  \nReceived: 13-11-2023 Revised: 23-11-2023 Accepted: 03-12-2023 Published: 13-12-2023  \nABSTRACT  \nMalaria, a life-threatening disease caused by Plasmodium parasites transmitted through infected mosquitoes, remains a significant public health concern in many regions worldwide. Early and accurate detection of malaria infection is crucial for timely treatment and disease management. The automated malaria detection system can be integrated into portable diagnostic devices, enabling healthcare professionals to perform rapid and accurate malaria tests in remote or resource-limited settings.The system can assist researchers and health organizations in tracking malaria prevalence and monitoring its spread, contributing to epidemiological studies and efficient resource allocation. Conventional methods for malaria detection involve manual examination of blood smears under a microscope by trained technicians. Although reliable, this process is time-consuming, labor-intensive, and dependent on the expertise of the microscopist. The regression-based examination of blood smears introduces the potential for errors, leading to false-negative or false-positive results. In recent years, machine learning-based approaches have shown promising results in automating the detection of malaria parasites through blood sample analysis. This work presents an advanced machine learning-based method for the automated detection of malaria infection, leveraging image processing techniques to achieve high accuracy and efficiency.  \nKeywords: malaria infection, blood samples, machine learning, epidemiological.  \n1. INTRODUCTION  \nMalaria infection is a widespread and potentially deadly disease caused by the Plasmodium parasite, transmitted to humans through the bite of infected female Anopheles mosquitoes. Diagnosis and monitoring of malaria often rely on the analysis of blood samples, which provides crucial insights into the presence and severity of the infection. When a blood sample is obtained from a patient suspected of having malaria, it undergoes a series of laboratory tests to confirm the diagnosis and assess the level of parasitic activity.The primary diagnostic method is the examination of a thin blood smear or a  \nJournal of Science and Technology  \nISSN: 2456-5660 Volume 8, Issue 12 (Dec-2023)  \n [www.jst.org.in](www.jst.org.in) DOI:[https://doi.org/10.46243/jst.2023.v8.i12.pp185-198](https://doi.org/10.46243/jst.2023.v8.i12.pp185-198)   \nthick blood smear under a microscope. Thin blood smears are used to identify the Plasmodium species responsible for the infection, while thick blood smears are employed to quantify the number of parasites present in the blood. This information is vital for determining the severity of the disease and guiding treatment decisions. Additionally, molecular techniques like polymerase chain reaction (PCR) can be employed to confirm the presence of the parasite and, in some cases, differentiate between species with high accuracy.Blood sample analysis also allows for the evaluation of other important parameters such as hematocrit levels, which help in ","cbCaibUElF5b3ePo","https://ap.wps.com/l/cbCaibUElF5b3ePo","pdf",1130956,1,14,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Diagnosis methods and limitations\n## Motivation for automated ML-based detection","[{\"question\":\"Why is early and accurate malaria detection important?\",\"answer\":\"Early detection enables prompt treatment, improves disease management, and reduces the risk of severe complications and death.\"},{\"question\":\"What is the conventional method for malaria diagnosis mentioned in the document?\",\"answer\":\"Diagnosis typically relies on manual examination of thin and thick blood smears under a microscope to identify Plasmodium species and quantify parasite load.\"},{\"question\":\"How does the proposed machine learning approach improve malaria detection?\",\"answer\":\"The approach automates detection using machine learning and image processing, aiming for faster, more accurate results while reducing time, labor, and dependence on skilled technicians.\"}]","Machine Learning-based Detection of Malaria Infection through Blood Sample Analysis - 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