Moment invariant features for automatic identification of critical malaria parasites

Ravendran, Ahalya; Silva, K.W.T. Roshali T. de; Senanayake, Ransalu

doi:10.1109/iciinfs.2015.7399058

Cited by 6 publications

(1 citation statement)

References 22 publications

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“…In the studies of image processing and automated classification procedure using a computer-aided system, many limitations have been reported by previous researchers. Most of the classification process only focused on the infected RBCs and normal RBCs classification systems [14,15], classification of PF, PM, PV and PO species with their life cycle stages [16], classification of PF, PM, PV and PO species without identifying the stages in malaria parasite [17], classification of the stage at PF [18][19][20], PV [21], PM and PO [22], and PF and PV species [23][24][25], respectively. No previous work has been reported on the automated classification of PK, PF and PV since PK is considered a new and regional species are infecting humans.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Classification System for Trophozoite Stages in Malaria Species

Kanafiah¹,

Mashor²,

Mohamed³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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Malaria is categorised as a dangerous disease that can cause fatal in many countries. Therefore, early detection of malaria is essential to get rapid treatment. The malaria detection process is usually carried out with a 100x magnification of thin blood smear using microscope observation. However, the microbiologist required a long time to identify malaria types before applying any proper treatment to the patient. It also has difficulty to differentiate the species in trophozoite stages because of similar characteristics between species. To overcome these problems, a computer-aided diagnosis system is proposed to classify trophozoite stages of Plasmodium Knowlesi (PK), Plasmodium Falciparum (PF) and Plasmodium Vivax (PV) as early species identification. The process begins with image acquisition, image processing and classification. The image processing involved contrast enhancement using histogram equalisation (HE), segmentation procedure using a combination of hue, saturation and value (HSV) color model, Otsu method and range of each red, green and blue (RGB) color selections, and feature extraction. The features consist of the size of infected red blood cell (RBC), brown pigment in the parasite, and texture using Gray Level Co-occurrence Matrix (GLCM) parts. Finally, the classification method using Multilayer Perceptron (MLP) trained by Bayesian Rules (BR) show the highest accuracy of 98.95%, rather than Levenberg Marquardt (LM) and Conjugate Gradient Backpropagation (CGP) training algorithms.

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Section: Introductionmentioning

confidence: 99%

An Intelligent Classification System for Trophozoite Stages in Malaria Species

Kanafiah¹,

Mashor²,

Mohamed³

et al. 2022

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

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Deep Learning Applications to Cytopathology: A Study on the Detection of Malaria and on the Classification of Leukaemia Cell-Lines

Gopakumar

Subrahmanyam

2019

Handbook of Deep Learning Applications

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Advances Towards Automatic Detection and Classification of Parasites Microscopic Images Using Deep Convolutional Neural Network: Methods, Models and Research Directions

Kumar

Arif

Alotaibi

et al. 2022

Arch Computat Methods Eng

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In the developing world, parasites are responsible for causing several serious health problems, with relatively high infections in human beings. The traditional manual light microscopy process of parasite recognition remains the golden standard approach for the diagnosis of parasitic species, but this approach is time-consuming, highly tedious, and also difficult to maintain consistency but essential in parasitological classification for carrying out several experimental observations. Therefore, it is meaningful to apply deep learning to address these challenges. Convolution Neural Network and digital slide scanning show promising results that can revolutionize the clinical parasitology laboratory by automating the process of classification and detection of parasites. Image analysis using deep learning methods have the potential to achieve high efficiency and accuracy. For this review, we have conducted a thorough investigation in the field of image detection and classification of various parasites based on deep learning. Online databases and digital libraries such as ACM, IEEE, ScienceDirect, Springer, and Wiley Online Library were searched to identify sufficient related paper collections. After screening of 200 research papers, 70 of them met our filtering criteria, which became a part of this study. This paper presents a comprehensive review of existing parasite classification and detection methods and models in chronological order, from traditional machine learning based techniques to deep learning based techniques. In this review, we also demonstrate the summary of machine learning and deep learning methods along with dataset details, evaluation metrics, methods limitations, and future scope over the one decade. The majority of the technical publications from 2012 to the present have been examined and summarized. In addition, we have discussed the future directions and challenges of parasites classification and detection to help researchers in understanding the existing research gaps. Further, this review provides support to researchers who require an effective and comprehensive understanding of deep learning development techniques, research, and future trends in the field of parasites detection and classification.

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Moment invariant features for automatic identification of critical malaria parasites

Cited by 6 publications

References 22 publications

An Intelligent Classification System for Trophozoite Stages in Malaria Species

An Intelligent Classification System for Trophozoite Stages in Malaria Species

Deep Learning Applications to Cytopathology: A Study on the Detection of Malaria and on the Classification of Leukaemia Cell-Lines

Advances Towards Automatic Detection and Classification of Parasites Microscopic Images Using Deep Convolutional Neural Network: Methods, Models and Research Directions

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