摘要
一位新闻记者兼机器人与机器学习公司的新闻编辑每日新闻-关于蚊子传播疾病的新研究-疟疾是一篇报道的主题。根据NewsRx记者在爱尔兰都柏林的新闻报道，研究表明：“疟疾是一种极恶性的疾病，由受感染的雌性蚊子叮咬引起。这种疾病不仅在人类中具有传染性，在动物中也具有传染性。”新闻记者从都柏林大学获得了这项研究的一句话:“疟疾会引起轻微的症状,如发烧、头痛、出汗和呕吐,以及肌肉不适;严重的症状包括昏迷、癫痫发作、癫痫发作等。”及时识别疟疾寄生虫对卫生人员来说是一项具有挑战性和挑战性的工作。一名专家技术人员通过显微镜检查感染的红细胞的血涂片。传统的识别疟疾的方法效率不高。机器学习方法对简单的分类挑战有效，但对复杂的任务无效。此外，机器学习涉及到严格的特征工程来训练模型和检测特征中的模式。另一方面,深度学习能够很好地处理复杂的任务,并能自动从图像中提取低层次和高层次的特征来检测疾病。本文介绍了一种基于深度学习的疟疾检测方法EfficientNet。本文提出了一种基于红细胞图像的DE-EP学习模型，并与预先训练的DE-EP学习模型进行了实验比较，同时还采用了K-Fold交叉验证来验证该方法的有效性。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Mosquito-Borne Disease s - Malaria is the subject of a report. According to news reporting from Dublin, Ireland, by NewsRx journalists, research stated, "Malaria is an extremely malig nant disease and is caused by the bites of infected female mosquitoes. This dise ase is not only infectious among humans, but among animals as well." The news correspondents obtained a quote from the research from University Colle ge Dublin, "Malaria causes mild symptoms like fever, headache, sweating and vomi ting, and muscle discomfort; severe symptoms include coma, seizures, and kidney failure. The timely identification of malaria parasites is a challenging and cha otic endeavor for health staff. An expert technician examines the schematic bloo d smears of infected red blood cells through a microscope. The conventional meth ods for identifying malaria are not efficient. Machine learning approaches are e ffective for simple classification challenges but not for complex tasks. Further more, machine learning involves rigorous feature engineering to train the model and detect patterns in the features. On the other hand, deep learning works well with complex tasks and automatically extracts low and high-level features from the images to detect disease. In this paper, EfficientNet, a deep learning-based approach for detecting Malaria, is proposed that uses red blood cell images. Ex periments are carried out and performance comparison is made with pre-trained de ep learning models. In addition, k-fold cross-validation is also used to substan tiate the results of the proposed approach."
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