Classification of Distinct Plasmodium Species in Thin Blood Smear Images using Kapur Segmentation Strategy

Abimala, T.; Rini, R. Joylin

doi:10.5958/0974-360x.2019.00385.8

Cited by 1 publication

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“…In the year 2012, Kaewkamnerd et al [80] proposed a system for automatically identifying and categorizing different types of the malaria parasite in thick bloodstains whereas Zhang et al [81] analyzed red blood cells for unlabeled malaria parasites in the year 2016. Khan et al [82] suggested a vision-based way to distinguish the malarial parasite from thick smear with the help of light microscopy images and Leishman stain by utilizing Kapur segmentation to categorize contaminated blood smears [83].…”

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

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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.

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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

show abstract

Classification of Distinct Plasmodium Species in Thin Blood Smear Images using Kapur Segmentation Strategy

Cited by 1 publication

References 0 publications

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

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

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