Automated malaria parasite and there stage detection in microscopic blood images

Charpe, Kshipra C.; Bairagi, Vinayak K.

doi:10.1109/isco.2015.7282336

Cited by 7 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Shape of blood cells becomes rigid due to presence of infection. Normally the size of infected cells are larger than normal cells [15]. Then obviously the surface area increases because of larger cells producing larger surface area and volume.…”

Section: Areamentioning

confidence: 99%

Blood image analysis to detect malaria using filtering image edges and classification

et al. 2019

View full text Add to dashboard Cite

Malaria is a most dangerous mosquito borne disease and its infection spread through the infected mosquito. It especially affects the pregnant females and Children less than 5 years age. Malarial species commonly occur in five different shapes, Therefore, to avoid this crucial disease the contemporary researchers have proposed image analysis based solutions to mitigate this death causing disease. In this work, we propose diagnosis algorithm for malaria which is implemented for testing and evaluation in Matlab. We use Filtering and classification along with median filter and SVM classifier. Our proposed method identifies the infected cells from rest of blood images. The Median filtering smoothing technique is used to remove the noise. The feature vectors have been proposed to find out the abnormalities in blood cells. Feature vectors include (Form factor, measurement of roundness, shape, count total number of red cells and parasites). Primary aim of this research is to diagnose malaria by finding out infected cells. However, many techniques and algorithm have been implemented in this field using image processing but accuracy is not up to the point. Our proposed algorithm got more efficient results along with high accuracy as compared to NCC and Fuzzy classifier used by the researchers recently.

show abstract

Section: Areamentioning

confidence: 99%

Blood image analysis to detect malaria using filtering image edges and classification

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Erythrocytes and the malaria parasite were categorized using geometric characteristics by Kumarasamy et al [73]. Charpe and Bairagi [74] proposed computerized malarial parasite and their stage discovery, which continues in advances like picture procurement, division, highlight extraction, and afterwards arrangement. The highlights: shading, shape, size, in-strained quality, the surface will be extricated, and arrangement is finished utilizing SVM.…”

Section: Classification With Species Identification Using Machine Lea...mentioning

confidence: 99%

Utilizing Image Analysis with Machine Learning and Deep Learning to Identify Malaria Parasites in Conventional Microscopic Blood Smear Images

Kundu,

Anguraj,

Bhattacharyya

2024

View full text Add to dashboard Cite

Malaria, a mosquito-borne blood infection, is caused by the Plasmodium genus. The traditional diagnostic approach relies on the manual examination of stained blood cells under a microscope. However, this labour-intensive and time-consuming process can be significantly improved with machine learning techniques to analyze microscopic images of blood smears for parasite detection. This paper reviews the various methodologies previously employed, focusing on the different strategies used for imaging. A comprehensive summary is provided, detailing the work conducted on both thin and thick blood smear images. Emerging developments in deep learning, coupled with contemporary mobile technologies, are also highlighted as potential future tools for malaria diagnosis. The paper further explores recent advancements in machine learning techniques for malaria detection and identification in images, emphasizing challenges associated with image processing. In addition, a detailed comparison is made between various machine learning approaches to provide a comprehensive overview. The application of these advanced machine learning and deep learning techniques holds the potential to revolutionize the process of malaria detection and control.

show abstract

Majority Voting Algorithm for Diagnosing of Imbalanced Malaria Disease

Sajana

Narasingarao

2019

Proceedings of the International Conference on ISMAC in Computational Vision and Bio-Engineering 2018 (ISMAC-CVB)

View full text Add to dashboard Cite

Automated malaria parasite and there stage detection in microscopic blood images

Cited by 7 publications

References 5 publications

Blood image analysis to detect malaria using filtering image edges and classification

Blood image analysis to detect malaria using filtering image edges and classification

Utilizing Image Analysis with Machine Learning and Deep Learning to Identify Malaria Parasites in Conventional Microscopic Blood Smear Images

Majority Voting Algorithm for Diagnosing of Imbalanced Malaria Disease

Contact Info

Product

Resources

About