Malaria detection using machine learning
Darshan Gowda B M
, Mrs.Sindhu B S , Hemanth U M , Darshan J , Amit Ashok Naik
malaria detection using machine learning
Malaria is mosquito-borne blood dyscrasia caused by parasites of the protoctist genus. Typical diagnostic tool for protozoal infection is that the examination of stained vegetative cell of patient in magnifier. The blood to be tested is placed in a very slide and is discovered below a magnifier to count the quantity of infected red blood cell.
Malaria remains a formidable public health concern, particularly in regions with limited resources. The Malaria Detector Application represents a technological innovation aimed at revolutionizing the diagnosis of this mosquito-borne disease. Employing advanced image processing algorithms and machine learning techniques, the application expedites the identification of malaria parasites in blood smear images, addressing the limitations of traditional diagnostic methods.
This abstract provides a concise overview of the Malaria Detector Application, outlining its objectives, development process, and potential impact on global healthcare. By combining accessibility with accuracy, the application aims to provide a scalable solution that can significantly improve the efficiency of malaria diagnosis, especially in resource-constrained settings. As the world continues its fight against malaria, this technological advancement holds promise for enhancing early detection and, consequently, the timely initiation of life-saving interventions. This report delves into the intricacies of the application, offering insights into its methodologies, challenges encountered, and its broader implications for public health.
"Malaria detection using machine learning", JNRID - JOURNAL OF NOVEL RESEARCH AND INNOVATIVE DEVELOPMENT (www.JNRID.org), ISSN:2984-8687, Vol.2, Issue 5, page no.a240-a243, May-2024, Available :https://tijer.org/JNRID/papers/JNRID2405027.pdf
Volume 2
Issue 5,
May-2024
Pages : a240-a243
Paper Reg. ID: JNRID_700335
Published Paper Id: JNRID2405027
Downloads: 000412
Research Area: Science and Technology
Country: MANDYA, Karnataka, India
ISSN: 2984-8687 | IMPACT FACTOR: 9.57 Calculated By Google Scholar | ESTD YEAR: 2023
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: JNRID (IJ Publication) Janvi Wave
