Fast k-Means Clustering Algorithm for Malaria Detection in Thick Blood Smear

Aris, Thaqifah Ahmad; Nasir, Aimi Salihah Abdul; Chin, Lim Chee; Jaafar, Harlina Suzana; Mohamed, Zeehaida

doi:10.1109/icset51301.2020.9265380

Cited by 8 publications

(5 citation statements)

References 8 publications

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“…The results show that the devices to use for image acquisition with the help of mobile devices, the objectives, depend on those objectives, their observations, different parameters, and the corresponding analysis. In 2019, Aris et al [149] scrutinized various chromatic constituents with the intent of ameliorating the efficacy of parasite enumeration predicated upon images of densely layered blood smears. Within the confines of this research inquiry, we have undertaken an exhaustive exploration of five discrete colour spaces, namely YCbCr, RGB, CMY, HSV, and HSL.…”

Section: Smartphones As Tools For Diagnosing Malaria In Remote Areasmentioning

confidence: 99%

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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: Smartphones As Tools For Diagnosing Malaria In Remote Areasmentioning

confidence: 99%

“…SVM [130], Neural Networks [149] Minsensitivity-80.5 [86], Max sensitivity-97.60 [120], Min specificity-93.8 [86], Max specificity-99.4 [8] Segmentation technique thresholding [86], Histogram thresholding [41,65,115],…”

Section: Giemsa [Light Microscope]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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“…The image resulting from the HSV transformation will be used for the segmentation stage, following equation 1. for HSV color [18]: The transformation of the RGB color space into HSV can be assumed that the coordinates R, G, B (0 or 1) are a sequence of red, green, blue in the RGB color space, max is the maximum value of the values (red, green, blue), min is the minimum value of value (red, green, blue). After converting the color space to HSV, the Hue component is used, because of the three HSV components that can visually distinguish between objects and the background, the Hue component is the Hue component where the Hue component is obtained from the HSV image.…”

Section: A Hsv Color Space Transformationmentioning

confidence: 99%

Convolutional Neural Network as an Image Processing Technique for Classification of Bacilli Tuberculosis Extra Pulmonary (TBEP) Disease

Riza¹,

Na’am²

2022

TEM Journal

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Tuberculosis Extra Pulmonary (TBEP) is an infectious disease caused by the bacterium Mycobacterium tuberculosis and can cause death. Patients suffering from this disease must be treated quickly without waiting for a long time. Biopsy is one of the techniques used to take the patient's lung fluid and given Ziehl Neelsen chemical dye and then observed using a microscope to determine this TBEP disease. In this research, the TBEP detection process was developed using a classification method, namely CNN with feature extraction and feature selection. The feature uses 5 features where these features are a combination of shape features and texture features with the highest information gain value. From the results of research conducted through the training and testing stages of the classification method using feature selection, the accuracy rate is higher than not using feature selection with a comparison of the feature selection stage increasing 0.6536% for the training process, and 0.8942% for the testing process.

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“…The image resulting from the HSV transformation will be used for the segmentation stage, following equation ( 1) for HSV color [19]: The transformation of the RGB color space into HSV can be assumed that the coordinates R, G, B (0 or 1) are a sequence of red, green, and blue in the RGB color space, min is the minimum value of value (red, green, blue). After converting the color space to HSV, the Hue component is used because the three HSV components can visually distinguish between objects and the background.…”

Section: Hsv Color Space Transformationmentioning

confidence: 99%

Comparison of K-Means Clustering and Otsu Thresholding Methods in the Detection of Tuberculosis Extra Pulmonary Bacilli in the HSV Color Space

Riza¹,

Na’am

Sumijan

2022

Indonesian J. Comput. Cybern. Syst.

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Tuberculosis Extra Pulmonary (TBEP) is an infectious disease caused by the bacterium Mycobacterium tuberculosis and can cause death. Patients suffering from this disease must be treated quickly without waiting long. Currently, anyone who will be detected caused by this bacterium takes a long time and costs a lot. The biopsy is one of the techniques used to take the patient's lung fluid and give Ziehl Neelsen chemical dye and then observe using a microscope to determine this TBEP disease. This research aims to help detect bacteria quickly and precisely by performing computer-aided image processing by creating an application system. The technique used is to develop the segmentation method. The segmentation process is to develop a Hue Saturation Value (HSV) color space transformation technique with the K-Means and Otsu Thresholding techniques. From the results of the two methods used, it turns out that the Otsu Thresholding method can detect TBEP results with more accuracy than the K-Means method. So the method developed is beneficial in accelerating and minimizing costs for detecting TBEP.

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Fast k-Means Clustering Algorithm for Malaria Detection in Thick Blood Smear

Cited by 8 publications

References 8 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

Convolutional Neural Network as an Image Processing Technique for Classification of Bacilli Tuberculosis Extra Pulmonary (TBEP) Disease

Comparison of K-Means Clustering and Otsu Thresholding Methods in the Detection of Tuberculosis Extra Pulmonary Bacilli in the HSV Color Space

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