论文标题
使用有效的检测器将寄生卵定位和分类在微观图像中
Localization and Classification of Parasitic Eggs in Microscopic Images Using an EfficientDet Detector
论文作者
论文摘要
原生动物和蠕虫寄生虫引起的IPI是人类在LMIC中最常见的感染之一。它们被认为是严重的公共卫生问题，因为它们会引起各种各样的潜在有害健康状况。研究人员一直在开发模式识别技术，以自动鉴定微观图像中的寄生虫卵。现有解决方案仍然需要改进以减少诊断错误并产生快速，高效和准确的结果。我们的论文解决了这一点，并提出了一个多模式学习检测器，以将寄生卵定位并将其分为11个类别。实验是在新型的Chula-Parasiteegg-11数据集上进行的，该数据集用于训练具有有效网络V2骨架和有效网络B7+SVM的效率电脑模型。该数据集有来自11个类别的11,000个显微镜培训图像。我们的结果表明表现出色，精度为92％，F1得分为93％。此外，IO分布说明了检测器的较高定位能力。
IPIs caused by protozoan and helminth parasites are among the most common infections in humans in LMICs. They are regarded as a severe public health concern, as they cause a wide array of potentially detrimental health conditions. Researchers have been developing pattern recognition techniques for the automatic identification of parasite eggs in microscopic images. Existing solutions still need improvements to reduce diagnostic errors and generate fast, efficient, and accurate results. Our paper addresses this and proposes a multi-modal learning detector to localize parasitic eggs and categorize them into 11 categories. The experiments were conducted on the novel Chula-ParasiteEgg-11 dataset that was used to train both EfficientDet model with EfficientNet-v2 backbone and EfficientNet-B7+SVM. The dataset has 11,000 microscopic training images from 11 categories. Our results show robust performance with an accuracy of 92%, and an F1 score of 93%. Additionally, the IOU distribution illustrates the high localization capability of the detector.
