这项工作（新学者研究资助，资助编号。RGNS 65 - 212）得到了高等教育、科学、研究和创新部常务秘书办公室（OPS MHESI）、泰国科学研究与创新部（TSRI）和King Mongkut's Institute of Technology Ladkrabang的财政支持。我们感谢泰国国家研究委员会（NRCT）[NRCT5-RSA63001-10]资助该研究项目。M.K.由泰国朱拉隆功大学科学研究与创新基金资助。我们还要感谢先进制造创新学院，King Mongkut's Institute of Technology，Ladkrabang，他们提供了深度学习平台和软件来支持该研究项目。

用于锥虫病检测和分类的 AI 程序

所有作者均无财务披露，也无利益冲突。

锥虫原生动物感染的显微镜观察是早期和常用的，特别是在偏远地区的监测期间，那里缺乏熟练的技术人员和劳动密集型和耗时的过程，这些都是及时报告卫生组织的障碍。尽管免疫学和聚合酶链反应（PCR）等分子生物学技术已被批准为高灵敏度方法，以支持实验室发现的有效性，但需要昂贵的化学品、仪器和专业人员来处理它们，这些技术大多位于大型医疗中心的中央实验室。共有的形态、混?...

锥虫病是全球健康问题面临的重大挑战，因为多种人畜共患物种引起人类疾病，其地理分布范围很广，分布在非洲和美洲大陆以外的地区，如南亚和东南亚1,2,3。非洲人类锥虫病 （HAT） 或昏睡病是由布氏冈比亚锥虫和罗得西亚锥虫引起的，它们分别产生慢性和急性形式，代表了非洲的主要传播。由于采采蝇的受感染唾液传播，致病寄生虫属于唾液组4.鉴于由克氏锥虫引起的众所周知的美国锥虫病（恰加斯病）一直是非流行国家的公共卫生问题;包括加拿大、美国、欧洲、澳大利亚和日本，因为个体经常从流行地区迁移5.锥虫感染属于 Stercoraria 组，因为它是由 reduviid 虫子的受感染粪便传播的。由埃文西锥虫感染引起的锥虫病和锥虫病（苏拉病）在非洲、南美洲、西亚和东亚以及南亚和东南亚国家流行 3,6。尽管已有由锥虫引起的人类锥虫病的报道 3,4,7,8,9,10,11,12，但寄生虫感染的传播途径存在争议：机械或受感染的血液通过食血昆虫（如采采蝇和塔巴尼德或马蝇）6,7， 8,9,10,12,13,14.泰国尚未发现病例报告，但已公布东部地区15 犬、赛马和水牛的 T. evansi 感染率很高16，这表明家畜之间会发生获得性传播。报道了几种由动物锥虫引起的非典型人类感染（间日锥虫、布氏锥虫、刚果锥虫、刘易斯锥虫和埃文西锥虫），这些感染不是人类锥虫的经典形式17。对非典型人类感染的认识可能被低估了，这突出表明需要改进诊断测试和实地调查，以发现和确认这些非典型病例，并允许适当控制和治疗影响全球牲畜、粮食安全和人类保健的动物致病性疾病18。这导致开发了一种与现有通用方法（显微镜检查）相结合的潜在策略，以便在主动监测期间快速筛查偏远地区的血液样本，从而能够确定限制和控制疾病的热点区域。
在多种家畜（如单峰骆驼、牛、马和狗）中偶发 Surra 病，可引起 euryxenous T. evansi 对人类的影响 1,4,13,14。人类感染似乎是不可能的，因为人血清中由sra样基因表达的锥虫溶解因子能够预防人布氏锥虫和刚果锥虫12,19。此外，正如印度的第一份病例报告所表明的那样，该疾病与免疫功能低下的艾滋病毒患者无关4。如上所述，可能的人类感染可能与锥虫溶解因子功能异常的高密度脂蛋白缺乏有关，这是一种罕见的常染色体隐性遗传病，即丹吉尔病4。2016 年，一名越南患者被发现具有两个野生型 APOL1 等位基因，血清 APOL1 浓度在正常范围内。然而，APOL-1 缺陷的理论不再被认为是有效的12.因此，锥虫感染的一种可能机制是在职业动物养殖过程中伤口与受感染的动物血液直接接触 4,12。显微镜检查显示，锥鞭毛体的形态是锥鞭毛体的单态形式，包括一种主要的细长、鞭毛和分裂锥虫，其与它们的相对种种布氏锥虫相似1,12,13。细胞核位于中心位置，后部位置有一个可见的小动质体。之前的一项研究表明，寄生虫可以以两种可比较的形式存在，称为经典形式和截断形式。然而，仍然有必要确认它们各自对宿主的致病作用20。症状过程各不相同，包括与发冷和出汗相关的间歇性发热。幸运的是，Suramin是早期人类非洲锥虫病的成功一线疗法，没有侵犯中枢神经系统（CNS），治愈了印度和越南的患者4,12,21。
除临床体征检查外，还存在几种针对 T. evansi 寄生虫的诊断方法，包括寄生虫学显微镜观察 4,9,12、血清学 4、8、9、10、12 和分子生物学试验 4,12.用 Giemsa 染色的薄血膜通常用于观察显微镜检查下存在的寄生虫，这是常规和常用的22。然而，该程序似乎是可行的;尽管如此，它既费时又费力，评估者之间的评估差异很大，仅对急性期敏感，并且需要个人实习生23。分子生物学和血清学检测也需要高技能的人员来执行样品制备的多个过程，包括提取和纯化样品，然后用昂贵的仪器进行测试，这很难标准化，有被寄生外物质污染的风险，以及结果的差异24.基于上述基本原理，需要快速和早期筛查技术来支持现场监测研究，并确保及时报告调查结果，以确定进一步控制疾病传播的热点地区1,8。基于计算机的设备 （CAD） 已被提议作为医学领域的创新技术，包括组织病理学和细胞病理学任务25。上面提到的CAD是高速执行的，并使用模式识别（即人工智能（AI））进行计算。人工智能方法是使用卷积神经网络算法完成的，该算法可用于处理大量数据集样本，特别是监督学习方法，该方法在数据消耗时训练训练有素的模型。
一般来说，人工智能是计算机解决需要专家智能的任务的能力，例如数据标记。机器学习 （ML） 是 AI 的一个子领域，它表示为一个计算机系统，具有两个不同的过程，包括特征提取和模式识别。深度学习 （DL） 或高级 ML 算法是指开发计算机化程序和设备，将类似人类的性能与更高和等于人类专业人员完成的精度水平进行比较26.目前，DL在医学和兽医领域的作用正在有希望地扩大和彻底改变传染病预防，目的是最近的预防并将其指导给个别卫生人员22,27。潜在的深度学习应用是无限的，具有质量标签和大量增强数据集，使专家能够腾出时间来管理项目任务。具体而言，数字图像的进步以及计算机辅助分析改善了报告的五类病理学的自动诊断和筛查;包括静态、动态、机器人、全玻片成像和混合方法28.有必要考虑到，深度学习算法方法和数字图像数据的整合可以鼓励当地工作人员在日常实践中利用该技术。
此前，使用混合模型的预测准确性的提高已被证明27.为了识别显微图像中的锥虫寄生虫，本研究提出了两种混合模型，结合了YOLOv4-tiny（目标检测）和Densenet201（目标分类）算法。在几种检测模型中，具有 CSPDarknet53 骨干的 YOLOv4-tiny 在定位和分类方面表现出高性能作为预测结果29。由于实时检测器修改了输入网络分辨率、卷积层数量、总参数和层输出数量之间的最佳平衡，因此与以前的版本相比，它改进了快速运行速度的优先级并针对并行计算进行了优化。密集卷积网络 （DenseNet） 是另一种流行的模型，可在竞争数据集中实现最先进的结果。DenseNet201 产生了与 ResNet101 类似的验证错误;但是，DenseNet201 的参数少于 2000 万个，少于 ResNet101 的 4000 多万个参数30。因此，DenseNet模型可以通过增加参数数量来提高预测精度，而不会出现过拟合的迹象。在这里，人工智能 （AI） 程序利用混合深度学习算法，在内部 CiRA CORE 平台上具有深度检测和分类神经网络主干。开发的程序可以从油浸显微图像中识别和分类原生动物锥虫物种，即 克氏锥虫、 布氏锥虫和 埃文西锥虫。该技术有可能通过提供快速、自动化和准确的筛查方法彻底改变疾病监测和控制。它可以帮助当地工作人员就寄生原生动物疾病的传播阻断策略做出更明智的决定。

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Darknet19, Darknet53 and Densenet201</td>
			<td>Gao Huang, Z. L., Laurens van der Maaten. Densely Connected Convolutional Networks. arXiv:1608.06993 [cs.CV]. (2016)</td>
			<td>https://github.com/liuzhuang13/DenseNet&#160;</td>
			<td>Deep convolutional neural network model that can function to&#160; classification 
			Generic name: YOLO model/ detection model?</td>
		</tr>
		<tr>
			<td>Olympus CX31 Model CX31RRBSFA</td>
			<td>&#160;Olympus, Tokyo, Japan</td>
			<td>SN 4G42178&#160;</td>
			<td>A light microscope&#160;</td>
		</tr>
		<tr>
			<td>Olympus DP21-SAL U-TV0.5XC-3</td>
			<td>&#160;Olympus, Tokyo, Japan</td>
			<td>SN 3D03838</td>
			<td>A digital camera 
			Generic name: Classification models/ densely CNNs</td>
		</tr>
		<tr>
			<td>Window 10</td>
			<td>Microsoft</td>
			<td>Window 10</td>
			<td>Operation system in computers</td>
		</tr>
		<tr>
			<td>YOLO v4-tiny&#160;</td>
			<td>Naing, K. M. et al. Automatic recognition of parasitic products in stool examination using object detection approach. PeerJ Comput Sci. 8 e1065, (2022).</td>
			<td>https://git.cira-lab.com/users/sign_in</td>
			<td>Deep convolutional neural network model that can function to both localization and also classification&#160;</td>
		</tr>
		<tr>
			<td></td>
			<td></td>
			<td>https://git.cira-lab.com/users/sign_in</td>
			<td></td>
		</tr>
	</tbody>
</table>

superior auto-identification of trypanosome parasites by using a hybrid deep-learning model

锥虫病是包括南亚和东南亚在内的世界多个地区的重大公共卫生问题。确定主动监测的热点地区是控制疾病传播的基本程序。显微镜检查是一种常用的诊断方法。然而，它主要依靠熟练和有经验的人员。为了解决这个问题，引入了一个人工智能 （AI） 程序，该程序在内部低代码 AI 平台 （CiRA CORE） 上利用对象识别和对象分类神经网络主干的混合深度学习技术。该程序可以从油浸显微图像中识别和分类原生动物锥虫物种，即 克氏锥虫、 布氏锥虫和 埃文西锥虫。人工智能程序利用模式识别来观察和分析单个血液样本中的多个原生动物，并使用注意力图突出显示每个寄生虫的细胞核和动质体作为特定特征。
为了评估 AI 程序的性能，创建了两个独特的模块，提供各种统计度量，例如准确性、召回率、特异性、精确度、F1 分数、误分类率、受试者工作特征 （ROC） 曲线以及精确率与召回率 （PR） 曲线。评估结果表明，人工智能算法在识别和分类寄生虫方面是有效的。通过提供快速、自动化和准确的筛查工具，该技术有可能改变疾病监测和控制。它还可以帮助地方官员就疾病传播阻断战略做出更明智的决定。

Trypanosomiasis is a significant challenge to global health issues due to a variety of zoonotic species causing human disease with a wide range of geographical distribution outside the African and American continents, such as South and Southeast Asia1,2,3. Human African trypanosomiasis (HAT) or sleeping sickness, is caused by Trypanosoma brucei gambiense and T. b. rhodesiense which produce the chronic and acute forms, respectively, representing the major spread in Africa. The causative parasite belongs to the Salivaria group due to the transmission by infected saliva of Tsetse flies4. Whereas, the well-known American trypanosomiasis (Chagas&#39;s disease) caused by T. cruzi has been a public health concern for non-endemic countries; including Canada, the USA, Europe, Australia, and Japan, because of the frequent migration of individuals from endemic areas5. The trypanosome infection belongs to the Stercoraria group because it is transmitted by the infected feces of reduviid bugs. The trypanosomiases and trypanosomoses (Surra disease) caused by the T. evansi infection are endemic in Africa, South America, Western and Eastern Asia, and South and Southeast Asian countries3,6. Although human trypanosomiasis caused by the trypanosome has been reported3,4,7,8,9,10,11,12, the route of transmission of the parasite infection is debated: either the mechanical or infected blood through hematophagous insects such as tsetse flies and tabanids or horse flies6,7,8,9,10,12,13,14. No case report has been found in Thailand, however, a high prevalence of the T. evansi infection in dog15, racing horses, and water buffalo in the eastern region has been published16, suggesting an acquired transmission between domestic animals would have occurred. Several atypical human infections caused by animal trypanosomes (T. vivax, T. b. brucei, T. congolense, T. lewisi, and T. evansi) were reported, which are not the classical forms of human trypanosomes17. Awareness about atypical human infections might be underestimated, highlighting the need for improved diagnostic tests and field investigations for detection and confirmation of these atypical cases, and allowing for proper control and treatment of animal pathogenic diseases that affect global livestock, food security18, and human healthcare. This led to the development of a potential strategy integrated with an existing common method (microscopic examination) to rapidly screen blood samples in remote areas during active surveillance, enabling the identification of the hotspot zones for restricting and controlling the disease.
Having a sporadic incidence of the Surra disease in a wide range of domestic animals such as dromedaries, cattle, equines, and dogs that evoke a euryxenous T. evansi may be zoonotic to humans1,4,13,14. Human infection seems impossible because a trypanolytic factor in human serum, expressed from a sra-like gene, is capable of preventing human T. brucei and T. congolense12,19. Furthermore, as the first case report from India demonstrates, the illness has no association with immunocompromised HIV patients4. As described above, the possible human infection may be related to a high-density lipoprotein deficiency with abnormal function of the trypanosome lytic factor, which is a rare autosomal recessive genetic disorder, namely Tangier disease4. In 2016, a Vietnamese patient was discovered to possess two wild-type APOL1 alleles and a serum APOL1 concentration within the normal range. However, the theory of APOL-1 deficiency is no longer considered valid12. Therefore, one possible mechanism of trypanosome infection is direct contact of a wound with infected animal blood during occupational animal farming4,12. Microscopic examination reveals that T. evansi morphology is a monomorphic form of the trypomastigote including a predominant long slender, flagellated, and dividing trypanosome which is similar to their relative species of T. brucei1,12,13. The nucleus is in the central position with a visible small kinetoplast in the posterior position. A previous study indicated that the parasite can exist in two comparable forms, known as the classical and truncated forms. However, it remains necessary to confirm their respective pathogenic effects on hosts20. The course of symptoms varies ranging from intermittent fever associated with chills and sweating. Suramin, fortunately, is a successful first-line therapy for early-stage human African trypanosomiasis with no invasion of the central nervous system (CNS), healing patients in India and Vietnam4,12,21.
Except for clinical sign examination, several diagnostic methods for T. evansi parasites exist, including parasitological microscopic observation4,9,12, serological4,8,9,10,12, and molecular biological tests4,12. Thin-blood films stained with Giemsa are often used to visualize the parasite present under microscopic examination, which is routinely and commonly used22. However, the procedure appears to be feasible; nonetheless, it is time-consuming and labor-intensive, has inter-rater assessment variability, is sensitive to only an acute phase, and requires a personal trainee23. Both molecular biology and serological testing also needed highly skilled personnel to perform multiple processes of sample preparation, including extracting and purifying the samples before testing them with expensive apparatus, which is difficult to standardize, risk of contamination with extra-parasitic materials, and discrepancies in results24. Based on the rationale described above, rapid and early screening technology is needed to support the field surveillance study and ensure that the survey result is reported in a timely manner to identify the hotspot zone for further control of the disease transmission1,8. Computerized-based devices (CAD) have been proposed as an innovative technology for medical fields, including histopathological and cytopathological tasks25. The CAD mentioned above was performed at high speed and computed using pattern recognition, namely, artificial intelligence (AI). The AI method is accomplished using convolutional neural network algorithms that can be used to deal with a large number of dataset samples, especially, a supervised learning approach that trains a well-trained model upon data consumption.
In general, AI is the ability of computers to solve tasks that require expert intelligence, such as data labeling. Machine learning (ML), a subfield of AI, is represented as a computer system with two different processes comprised of feature extraction and pattern recognition. Deep learning (DL), or advanced ML algorithms, refers to the development of computerized programs and devices comparing human-like performance with levels of accuracy greater and equal to that accomplished by human professionals26. Currently, the role of DL in medical and veterinary fields is promisingly expanding and revolutionizing communicable disease prevention with the aim of recent prevention and guiding it to individual health staff22,27. The potential DL application is limitless with quality labels and a large number of augmented datasets, freeing specialists to manage the project task. Specifically, an advance in the digital image along with computer-assisted analysis, improved the automatic diagnostic and screening in five categories of pathology reported; including static, dynamic, robotic, whole slide imaging, and hybrid methods28. It is necessary to consider that the integration of DL algorithm approaches and digital image data could encourage local staff to utilize the technology in their daily practices.
Previously, the increase in prediction accuracy of using a hybrid model had been proven27. To identify the trypanosome parasite in microscopic images, this research presents two hybrid models, incorporating the YOLOv4-tiny (object detection) and Densenet201 (object classification) algorithms. Among several detection models, YOLOv4-tiny with a CSPDarknet53 backbone showed high performance as a prediction result in terms of localization and classification29. Since the real-time detector has modified the optimal balance among the input network resolution, the amount of the convolutional layer, the total parameter, and the number of layer outputs, it has improved prioritizing fast operating speeds and optimizing for parallel computations when compared to previous versions. Dense Convolutional Network (DenseNet) is another popular model that achieves state-of-the-art results across competitive datasets. DenseNet201 yielded a similar validation error comparable to that of ResNet101; however, DenseNet201 has fewer than 20 million parameters, which is less than ResNet101&#39;s more than 40 million parameters30. Therefore, the DenseNet model could improve prediction accuracy with an increasing number of parameters with no sign of overfitting. Here, an artificial intelligence (AI) program utilizes a hybrid deep learning algorithm with deep detection- and classification neural network backbones on the in-house CiRA CORE platform. The developed program can identify and classify the protozoan trypanosome species, namely Trypanosoma cruzi, T. brucei, and T. evansi, from oil-immersion microscopic images. This technology has the potential to revolutionize disease surveillance and control by providing a rapid, automated, and accurate screening method. It could aid local staff in making more informed decisions on transmission-blocking strategies for parasitic protozoan disease.

作者聚焦：从显微图像中检测 AI 驱动的锥虫物种 

使用混合深度学习模型对锥虫寄生虫进行卓越的自动识别

The goal of the research is to develop an artificial intelligence AI model that makes use of a hybrid deep learning technique of object detection and classification backbones for defining the protozoa trypanosome specie, namely trypanosoma cruzi, T.brucei, and T.evansi, from oil immersion, microscopic images on the in-house, low-code AI platform CiRA CORE. The most recent development in our field of research is the development of the learning algorithms that can identify and classify trypanosome species from microscopic images. This program has the potential to revolutionize the surveillance and control by providing a rapid automated and accurate screening method that can be used by local staff in remote areas.Using the modified and hybrid algorithms between two different deep learning models within the purpose AI program can overcome many challenges. Identification of shared morphology, mixed and immature infection, and precise species characterization enables automatic standard taxonomy.

在这项研究中，提出了混合深度学习算法来帮助自动预测锥虫寄生虫感染的血液样本的阳性性。使用基于暗网骨干神经网络的物体检测算法，对存档的 Giemsa 染色血片进行分类，以定位和分类寄生与非寄生。在前人模型得到的任意矩形框预测结果中，开发了最佳选择的分类模型，对布 氏锥虫、 克氏锥虫 和 埃文西锥虫等3种具有医学和兽医意义的锥虫进行分类。所使用的混合模...

Watch this Scientific Journal Video about AI-Driven Trypanosome Species Detection from Microscopic Images at JoVE.com

在低代码 AI 平台上使用简单的步骤自动筛选全球医用血液寄生虫。通过在混合深度学习模型中使用目标检测和分类方法，改善了血膜的前瞻性诊断。主动监测和训练有素的模型的协作有助于识别锥虫传播的热点。

Trypanosomiasis is a significant public health problem in several regions across the world, including South Asia and Southeast Asia. The identification of ...

该研究的目标是开发一种人工智能人工智能模型，该模型利用目标检测和分类骨干的混合深度学习技术来定义原生动物锥虫物种，即克氏锥虫、布氏锥虫和埃文西锥虫，来自内部低代码人工智能平台CiRA CORE上的油浸、显微图像。我们研究领域的最新发展是开发了学习算法，该算法可以从显微图像中识别和分类锥虫物种。该计划有可能通过提供一种快速、自动化和准确的筛查方法，供偏远地区的当地工作人员使用，从而彻底改变监测和控制。在目的AI程序中，在两个不同的深度学习模型之间使用修改和混合算法可以克服许多挑战。识别共同形态、混合和未成熟感染以及精确的物种表征可实现自动标准分类。

superior-auto-identification-trypanosome-parasites-using-hybrid-deep

Research

JoVE Journal

Bioengineering

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

885 次观看.  King Mongkut’s Institute of Technology Ladkrabang. 该研究的目标是开发一种人工智能人工智能模型，该模型利用目标检测和分类骨干的混合深度学习技术来定义原生动物锥虫物种，即克氏锥虫、布氏锥虫和埃文西锥虫，来自内部低代码人工智能平台CiRA CORE上的油浸、显微图像。我们研究领域的最新发展是开发了学习算法，该算法可以从显微图像中识别和分类锥虫物种。该计划有可能通过提供一种快速、自动化和准确的筛查?...

视频: 作者聚焦：从显微图像中检测 AI 驱动的锥虫物种 

Trypanosomiasis is a significant public health problem in several regions across the world, including South Asia and Southeast Asia. The identification of hotspot areas under active surveillance is a fundamental procedure for controlling disease transmission. Microscopic examination is a commonly used diagnostic method. It is, nevertheless, primarily reliant on skilled and experienced personnel. To address this issue, an artificial intelligence (AI) program was introduced that makes use of a hybrid deep learning technique of object identification and object classification neural network backbones on the in-house low-code AI platform (CiRA CORE). The program can identify and classify the protozoan trypanosome species, namely Trypanosoma cruzi, T. brucei, and T. evansi, from oil-immersion microscopic images. The AI program utilizes pattern recognition to observe and analyze multiple protozoa within a single blood sample and highlights the nucleus and kinetoplast of each parasite as specific characteristic features using an attention map.
To assess the AI program&#39;s performance, two unique modules are created that provide a variety of statistical measures such as accuracy, recall, specificity, precision, F1 score, misclassification rate, receiver operating characteristics (ROC) curves, and precision versus recall (PR) curves. The assessment findings show that the AI algorithm is effective at identifying and categorizing parasites. By delivering a speedy, automated, and accurate screening tool, this technology has the potential to transform disease surveillance and control. It could also assist local officials in making more informed decisions on disease transmission-blocking strategies.

Worldwide medical blood parasites were automatically screened using simple steps on a low-code AI platform. The prospective diagnosis of blood films was improved by using an object detection and classification method in a hybrid deep learning model. The collaboration of active monitoring and well-trained models helps to identify hotspots of trypanosome transmission.

科研

生物工程

Training Process CiRA CORE Platform and Object Detection Model Evaluation to Identify and Classify the Protozoan Trypanosome Species

To begin, move the deep train function from the general toolbar onto the screen to initiate data labeling and training. Click on the load images button within the deep train tool settings and navigate to the directory containing the images to import and label images. Then click and hold the left mouse button to label the objects and assign names to the selected objects.Click save GT to save a dot GT file in the same directory. Before starting the model training, click the gen setting button to access data augmentation features. Utilize four types of augmentations, namely rotation, contrast, noise, and blur to enrich the dataset.To begin the model training, click on the training button located within the deep train tool. In the generate training files function, choose the models, batch size, and subdivisions. Click the generate button to generate data and save it in the directory.After configuring all settings, click the train button to initiate the model training. The program will automatically execute and adjust the dataset weight while it assesses the training loss. Upon achieving optimal loss, click the export button and save the weight file in the directory.To proceed with object detection model evaluation, select evaluate in the plugin toolbar and move the eval detect function onto the screen. Afterward, click on the setting button. Wait for the three functions, detection, evaluate, and plot to appear.To start the model evaluation, click the load config button and import the trained wait file from the directory. To import test images from the image file directory, click the browse button. Then click on the load GT button to import the GT file.Next, click the evaluation button to assess the detection model in the directory. Upon completion, the results will be automatically saved as a CSV file in the same directory, sorted by class name, containing key parameters, such as true positive, false positive, false negative, recall, and precision for each class. To generate the precision recall or PR curve, navigate to the plot function.Click on browse to import the CSV files from the directory. Select the desired classes from the list and click the plot button to visualize the PR curve. Click on the save button to save an image with AUC values of the PR curve in the required image format at the selected directory.To select the image classification model training function, navigate to the image toolbar, select deep classif, and move classif train onto the screen. To import images for training, click on the open folder button within the classif train tool settings and navigate to the directory containing the images. Before training, enrich the dataset by clicking on the augmentation button and applying techniques such as rotation, contrast, flipping, noise, and blur.To initiate the model training process, click the gen train button within the classif train tool. Under gen train, select the desired models, batch size, and subdivisions. Assign a directory to save the generated data and click the generate button.Once all configurations are complete, click on the start button. The program will run continuously, assessing the training loss, and adjusting the weight of the dataset as needed. Upon achieving the desired loss level, click the export button and store the weight file in the specified directory.

训练过程 CiRA CORE平台和目标检测模型评估，以识别和分类原生动物锥虫物种

Image Classification as Model Training and Model Evaluation to Identify and Classify the Protozoan Trypanosome Species

To begin, navigate to the plugin toolbar. Then drag and drop the EvaluateClassif function onto the screen. Click on the evaluate button located within the EvaluateClassif tool.Click the load folder image button to import test images, and load config to import the trained weight file from the directory. Then click the start button to evaluate the classification model. After evaluation, click the export to CSV button to save the results as a CSV file in the directory.To evaluate data at every threshold, click on the start all threshold to save the CSV file in the directory with class names, including parameters such as recall, true positive rate, false positive rate, and precision for each class. To plot the receiver operating characteristics curve, click on the plot ROC button located within the EvaluateClassif tool. Click on the browse button and import the CSV files from the directory.Inspect the imported class list and select each class label to plot the ROC curve. Next, to visualize the ROC curve, click on the plot button. Make any desired adjustments to the image properties, such as font size, font colors, rounding decimals, line styles, and line colors.Finally, click the save button to save the ROC curve image with the AUC values in the required image format in the directory.

图像分类作为模型训练和模型评估，以识别和分类原生动物锥虫物种

Hybrid (Detection and Classification) as Model Testing to Identify and Classify the Protozoan Trypanosome Species

To begin, navigate to the general toolbar, click on Button Run, then click Debug to start the hybrid testing model. Next, move to the image toolbar, choose Acquisition and select Image Slide. Go to Deep Composite and click on Deep D-C.Before beginning the testing phase for the images, click on the setting option available in the image slide tool and import the test images. Then click the setting option in the deep D-C tool to import the two saved trained weight files. For the Detect function, click on the Config button, plus button, and select the backend CUDA or CPU.Provide a name and click OK.Select the weight file directory and click Choose. For the Classif function, click the Config button, followed by the plus button, and then select the backend. Provide a name and click OK.After this, select the directory for the weight file and click Choose.To view the test image results, click on the image function available in the Debug tool. Lastly, to evaluate the predicted results for each image, click on the Run button found in the Button Run tool.