Author:
P.RengaprabhuPublished in
Journal of Science Technology and Research( Volume , Issue )
1. Priyamvada Jain, BabinaChakma, SanjuktaPatra, and PranabGoswami, “Potential Biomarkers and Their Applications for Rapid and Reliable Detection of Malaria”, Hindawi Publishing Corporation BioMed Research International Volume 2014, Article ID 852645, pp1-20, 2014.
2. YashasviPurwar, Sirish L Shah, Gwen Clarke, AreejAlmugairi and AtisMuehlenbachs, “Automated and unsupervised detection of malaria parasites in microscopic images”, Springer-Malaria Journal, Vol. 10, pp 11-22, 2019.
4. Muhammad Akram., et al., (2020)., A Geographical Review: Novel Coronavirus (COVID-19) Pandemic. Asian Journal of Applied Science and Technology (AJAST). 4(4): 44-50.
5. Muhammad Akram., et al., (2020)., ‘Anti-Viral Medicinal Plants & Their Chemical Constituents, Experimental and Clinical Pharmacology of Antiviral Plants’. Journal of Science Technology and Research (JSTAR)., 1(1): 1-17.
6. M.Prakash, U. Gowshika, D.Shaloom Immulicate, S.Sathiya Priya, “Analysis of Defect in Dental Using Image Processing”, International Journal of Applied Engineering Research, Vol 10, No. 9, 2015, pp 8125-8129.
7. Karthick, R., 2019. Design of Low Power MPSoC Architecture using DR Method. Asian Journal of Applied Science and Technology (AJAST), 3(2), pp.101-104.
8. Sathiyanathan, N., 2018. Medical Image Compression Using View Compensated Wavelet Transform. Journal of Global Research in Computer Science, 9(9), pp.01-04.
ABSTRACT:
The Mosquito Auto Identification Scheme using Image Extraction Techniques is designed to improve the detection of parasitic infections like malaria by automating the identification of mosquito species and the presence of parasites such as Plasmodium falciparum. Accurate detection is essential to avoid misdiagnosis, which can lead to severe health complications. Traditional methods often rely on manual blood smear analysis, which may introduce human error. In this system, image processing algorithms are employed to identify parasites in Giemsa-stained thin blood films. Two key approaches are used: segmentation-based recognition and feature extraction with minimum distance classifiers. These techniques enhance sensitivity, specificity, and predictive accuracy compared to manual detection. The proposed scheme ensures a higher level of reliability in identifying the infection, aiding in better diagnosis and timely treatment.
INTRODUCTION:
Malaria is a serious disease that affects millions of people worldwide, especially in tropical and subtropical areas. It spreads through the bite of female Anopheles mosquitoes, which carry Plasmodium parasites. Once inside the human body, these parasites grow and multiply, leading to infection. The number of infected red blood cells, known as parasitaemia, helps doctors understand how severe the illness is. This measurement also guides treatment decisions.Doctors usually check for malaria by examining blood samples under a microscope. However, this manual method takes time and can lead to errors, especially when many samples need to be tested. To solve this problem, researchers introduced the Mosquito Auto Identification Scheme using Image Extraction Techniques. This system uses image processing tools to identify malaria parasites in blood smears quickly and accurately.In addition, it uses smart algorithms like Support Vector Machines (SVM) and K-means clustering.
