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利用血涂片图像进行疟疾寄生虫自动分析的计算方法:最新进展。

Computational Methods for Automated Analysis of Malaria Parasite Using Blood Smear Images: Recent Advances.

机构信息

Chitkara University School of Computer Applications, Chitkara University, Himachal Pradesh, India.

School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India.

出版信息

Comput Intell Neurosci. 2022 Apr 11;2022:3626726. doi: 10.1155/2022/3626726. eCollection 2022.

DOI:10.1155/2022/3626726
PMID:35449742
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9017520/
Abstract

Malaria comes under one of the dangerous diseases in many countries. It is the primary reason for most of the causalities across the world. It is presently rated as a significant cause of the high mortality rate worldwide compared with other diseases that can be reduced significantly by its earlier detection. Therefore, to facilitate the early detection/diagnosis of malaria to reduce the mortality rate, an automated computational method is required with a high accuracy rate. This study is a solid starting point for researchers who want to look into automated blood smear analysis to detect malaria. In this paper, a comprehensive review of different computer-assisted techniques has been outlined as follows: (i) acquisition of image dataset, (ii) preprocessing, (iii) segmentation of RBC, and (iv) feature extraction and selection, and (v) classification for the detection of malaria parasites using blood smear images. This study will be helpful for: (i) researchers can inspect and improve the existing computational methods for early diagnosis of malaria with a high accuracy rate that may further reduce the interobserver and intra-observer variations; (ii) microbiologists to take the second opinion from the automated computational methods for effective diagnosis of malaria; and (iii) finally, several issues remain addressed, and future work has also been discussed in this work.

摘要

疟疾是许多国家的危险疾病之一。它是世界上大多数死亡的主要原因。与其他可以通过早期发现显著降低死亡率的疾病相比,目前疟疾的高死亡率在全球范围内被认为是一个重大原因。因此,为了促进疟疾的早期发现/诊断以降低死亡率,需要一种具有高准确率的自动化计算方法。本研究为希望研究自动化血涂片分析以检测疟疾的研究人员提供了一个坚实的起点。本文全面概述了不同的计算机辅助技术,如下所示:(i)图像数据集的获取,(ii)预处理,(iii)红细胞的分割,以及(iv)使用血涂片图像进行特征提取和选择,以及(v)分类以检测疟原虫。本研究将有助于:(i)研究人员可以检查和改进现有的计算方法,以实现疟疾的早期诊断,具有较高的准确率,从而进一步减少观察者间和观察者内的差异;(ii)微生物学家可以从自动化计算方法中获得第二个意见,以有效诊断疟疾;(iii)最后,这项工作还讨论了一些仍待解决的问题和未来的工作。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fca/9017520/9201e3b01110/CIN2022-3626726.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fca/9017520/887fad11c3dc/CIN2022-3626726.001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fca/9017520/9201e3b01110/CIN2022-3626726.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fca/9017520/887fad11c3dc/CIN2022-3626726.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fca/9017520/f44a3c584524/CIN2022-3626726.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fca/9017520/0bfa321653ee/CIN2022-3626726.003.jpg
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