{"title":"基于特征识别的疟疾寄生虫识别","authors":"","doi":"10.46243/jst.2020.v5.i3.pp248-250","DOIUrl":null,"url":null,"abstract":"Malaria is one in all the life threatening diseases. Diagnosis of diseases like malaria is very hooked in to\nthe identification of parasites in blood. Various methods are applied for this process. The majority of all method\nuses machine learning to identify the malarial parasites. This method has shortcomings in long training time and\nalso the must be retrained if a replacement data emerged. Among all of the other various methods that are used,\nidentification using feature based recognition is likely to be rarely used. This method is powerful within the term\nthat it doesn't require training process, but only an image sample from which the feature are visiting be extracted.\nDuring this paper, we design an identification process for blood parasites using one all told the famous local\nfeature extraction algorithms, i.e. SURF (Speeded-Up Robust Features). In our experiment, we evaluate the system\nto spot Plasmodium parasites. During this experiment, we are focusing only on parasite’s gametocyte stage. Here,\nwe use the system to spot whether or not the parasite is Plasmodium falciparum, Plasmodium malariae,\nPlasmodium ovale, or Plasmodium vivax.","PeriodicalId":23534,"journal":{"name":"Volume 5, Issue 4","volume":"332 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Malaria Parasite Identification using Feature Based Recognition\",\"authors\":\"\",\"doi\":\"10.46243/jst.2020.v5.i3.pp248-250\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Malaria is one in all the life threatening diseases. Diagnosis of diseases like malaria is very hooked in to\\nthe identification of parasites in blood. Various methods are applied for this process. The majority of all method\\nuses machine learning to identify the malarial parasites. This method has shortcomings in long training time and\\nalso the must be retrained if a replacement data emerged. Among all of the other various methods that are used,\\nidentification using feature based recognition is likely to be rarely used. This method is powerful within the term\\nthat it doesn't require training process, but only an image sample from which the feature are visiting be extracted.\\nDuring this paper, we design an identification process for blood parasites using one all told the famous local\\nfeature extraction algorithms, i.e. SURF (Speeded-Up Robust Features). In our experiment, we evaluate the system\\nto spot Plasmodium parasites. During this experiment, we are focusing only on parasite’s gametocyte stage. Here,\\nwe use the system to spot whether or not the parasite is Plasmodium falciparum, Plasmodium malariae,\\nPlasmodium ovale, or Plasmodium vivax.\",\"PeriodicalId\":23534,\"journal\":{\"name\":\"Volume 5, Issue 4\",\"volume\":\"332 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Volume 5, Issue 4\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.46243/jst.2020.v5.i3.pp248-250\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Volume 5, Issue 4","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.46243/jst.2020.v5.i3.pp248-250","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
疟疾是所有威胁生命的疾病之一。疟疾等疾病的诊断非常依赖于血液中寄生虫的识别。在这个过程中应用了各种方法。大多数方法使用机器学习来识别疟疾寄生虫。该方法存在训练时间长、出现替换数据时需要重新训练的缺点。在使用的所有其他各种方法中,使用基于特征的识别的识别可能很少使用。这种方法的强大之处在于它不需要训练过程,而只需要从一个图像样本中提取特征。本文采用一种著名的局部特征提取算法SURF (accelerated - up Robust Features,加速鲁棒特征)设计了一种血液寄生虫的识别过程。在我们的实验中,我们评估了发现疟原虫的系统。在这个实验中,我们只关注寄生虫的配子体阶段。在这里,我们使用该系统来识别寄生虫是否为恶性疟原虫、疟疾疟原虫、卵形疟原虫或间日疟原虫。
Malaria Parasite Identification using Feature Based Recognition
Malaria is one in all the life threatening diseases. Diagnosis of diseases like malaria is very hooked in to
the identification of parasites in blood. Various methods are applied for this process. The majority of all method
uses machine learning to identify the malarial parasites. This method has shortcomings in long training time and
also the must be retrained if a replacement data emerged. Among all of the other various methods that are used,
identification using feature based recognition is likely to be rarely used. This method is powerful within the term
that it doesn't require training process, but only an image sample from which the feature are visiting be extracted.
During this paper, we design an identification process for blood parasites using one all told the famous local
feature extraction algorithms, i.e. SURF (Speeded-Up Robust Features). In our experiment, we evaluate the system
to spot Plasmodium parasites. During this experiment, we are focusing only on parasite’s gametocyte stage. Here,
we use the system to spot whether or not the parasite is Plasmodium falciparum, Plasmodium malariae,
Plasmodium ovale, or Plasmodium vivax.