Application of Machine and Deep Learning Algorithms in Optical Microscopic Detection of Plasmodium Parasites: A Malaria Diagnostic Tool for the Future.

Lots of people all over the world is threaten by a popular blood infection illness that is called as malaria. According to this fact, immediate diagnosis tests are essential to avoid the malaria parasites from expanding in every part of the body. Malaria detection is based on parasitic count process on thick blood smear samples. Anyhow, this mechanism consist the chances of misinterpretation of parasites on behalf to human flaws. Thus, this research objective is to investigate the segmentation performance for improving malaria detection in thick blood smear images through fast k-means clustering algorithm on various color models. In this research, fast k-means clustering is used because of its advantage which is no need to retrain cluster center that causes time taken to train the image cluster centers is reduce. Meanwhile, different color models have been utilized in order to identify the most relevant color model that obviously highlight the parasites. Five varied color models namely RGB, XYZ, HSV, YUV and CMY are selected and 15 color components namely R, G, B, X, Y, Z, H, S, V, Y, U, V, C, M and Y component have been derived with the aim to discover which color component is the topnotch for malaria parasites detection. In general, around 100 thick blood smear images have been tested in this study and the outcomes reveal that the best segmentation performance is segmentation through R component of RGB with 99.81% accuracy.

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

Malaria Parasite Diagnosis Using Computational Techniques: A Comprehensive Review

Malaria is a pressing medical issue in tropical and subtropical regions. Currently, the manual microscopic examination remains the gold standard malaria diagnosis method. Nevertheless, this procedure required highly skilled lab technicians to prepare and examine the slides. Therefore, a framework encompassing image processing and machine learning is proposed due to inconsistencies in manual inspection, counting, and staging. Here, a standardized segmentation framework utilizing thresholding and clustering is developed to segment parasites’ stages of P. falciparum and P. vivax species. Moreover, a multi-stage classifier is designed for recognizing parasite species and staging in both species. Experimental results indicate the effectiveness of segmenting thick smear images based on Phansalkar thresholding garnered an accuracy of 99.86%. The employment of variance and new transferring process for the clustered members, enhanced k-means (EKM) clustering has successfully segmented all malaria stages with accuracy and an F1-score of 99.20% and 0.9033, respectively. In addition, the accuracies of parasite detection, species recognition, and staging obtained through a random forest (RF) accounted for 86.89%, 98.82%, and 90.78%, respectively, simultaneously. The proposed framework enables versatile malaria parasite detection and staging with an interactive result, paving the path for future improvements by utilizing the proposed framework on all others malaria species.

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Robust Image Processing Framework for Intelligent Multi-Stage Malaria Parasite Recognition of Thick and Thin Smear Images

Helminthiases disease has been diagnosed as one of the most common health problems in children and adults that is caused by human intestinal parasites named helminth. An immediate diagnosis system is required for the identification and classification of helminth to avoid the development of parasites in human intestine. In response to the needs for a rapid and precise identification of helminth, this research proposes analyzing the segmentation performance analysis of unsupervised colour image segmentation of helminth parasites through enhanced $k$ -means clustering (EKM) algorithm on the modified global contrast stretching (MGCS) and the modified linear contrast stretching (MLCS) enhancement techniques. By segmenting the helminth parasites using EKM clustering algorithm, the concept of variance and a new version of transferring process for clustered members are used to assist the assignation of data to the proper centre during the process of clustering, so that good segmented image can be obtained. A total of 100 helminth parasites images have been analyzed with the approach comprising of 50 images from each species of the Ascaris Lumbricoides Ova (ALO) and the Trichuris Trichiura Ova (TTO). Overall, the average results generated by the enhanced $k$ -means clustering algorithm on MGCS is higher than MLCS. The proposed technique achieved 99.88% accuracy, 95.68% sensitivity alongside 99.95% specificity. Ultimately, the result demonstrates that the proposed technique is suitable for image segmentation of the helminth parasites images.

Enhanced $k$-Means Clustering Algorithm for Detection of Human Intestinal Parasites

Malaria is a life-threatening disease caused by parasites that are transmitted to people through the bite of female anopheles mosquito. Malaria is usually found in tropical and subtropical climates where the parasites that cause it live. Once the parasite enters the human body, it lodges itself in the liver where it multiplies approximately 10, 000 times. The diagnosis of malaria is done by various methods which include thick and thin smear method, rapid diagnostic test, antigen detection etc. The limitations of these methods includes false positive rate. This paper presents a methodology to identify the malarial parasites so that the false positive rate is reduced. The color based discrimination is one of the most feasible methods as it shows greater accuracy and efficiency. The K-means clustering technique is followed for color based discrimination. The segmented malarial cell from K-means clustering is further subjected to morphological operations to extract the feature for classification. The Results show that the proposed method helps in the detection of infected red blood cells thus improving decisionmaking for malaria diagnosis.

Detection of Malarial Parasites using Image Processing Techniques from Blood Smear Slides

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Detection of Malarial Parasites using Image Processing Techniques from Blood Smear Slides