According to the World Malaria Report of 2022, 247 million cases of malaria and 619,000 related deaths were reported in 2021. This highlights the predominance of the disease, especially in the tropical and sub-tropical regions of Africa, parts of South-east Asia, Central and Southern America. Malaria is caused due to the Plasmodium parasite which is circulated through the bites of the female Anopheles mosquito. Hence, the detection of the parasite in human blood smears could confirm malarial infestation. Since the manual identification of Plasmodium is a lengthy and time-consuming task subject to variability in accuracy, we propose an automated, computer-aided diagnostic method to classify malarial thin smear blood cell images as parasitized and uninfected by using the ResNet50 Deep Neural Network. In this paper, we have used the pre-trained ResNet50 model on the open-access database provided by the National Library of Medicine's Lister Hill National Center for Biomedical Communication for 150 epochs. The results obtained showed accuracy, precision, and recall values of 98.75%, 99.3% and 99.5% on the ResNet50(proposed) model. We have compared these metrics with similar models such as VGG16, Watershed Segmentation and Random Forest, which showed better performance than traditional techniques as well.

翻译：据《2022年世界疟疾报告》统计，2021年全球共报告2.47亿例疟疾病例及61.9万例相关死亡病例。这一数据凸显了该疾病的高发性，尤其在非洲热带与亚热带地区、东南亚部分地区、中美洲与南美洲地区尤为突出。疟疾由疟原虫引起，通过雌性按蚊叮咬传播。因此，在人体血液涂片中检测疟原虫可确认疟疾感染。鉴于人工识别疟原虫耗时长、工作量大且准确率波动性大，我们提出一种基于ResNet50深度神经网络的自动化计算机辅助诊断方法，用于将薄血涂片细胞图像分类为感染与未感染两类。本研究采用美国国家医学图书馆Lister Hill国家生物医学交流中心提供的开源数据库，对预训练ResNet50模型进行了150个周期的训练。实验结果表明，我们提出的ResNet50模型在准确率、精确率和召回率上分别达到98.75%、99.3%和99.5%。我们将这些指标与VGG16、分水岭分割及随机森林等同类模型进行了对比，结果显示本方法较传统技术具有更优性能。