Abstract
Fecal-contaminated water causes diseases and even death. Current microbial water safety tests require pathogen incubation, taking 24-72 hours and costing \$20-\$50 per test. This paper presents a solution (DeepScope) exceeding UNICEF's ideal Target Product Profile requirements for presence/absence testing, with an estimated per-test cost of \$0.44. By eliminating the need for pathogen incubation, DeepScope reduces testing time by over 98\%. In DeepScope, a dataset of microscope images of bacteria and water samples was assembled. An innovative augmentation technique, generating up to 21 trillion images from a single microscope image, was developed. Four convolutional neural network models were developed using transfer learning and regularization techniques, then evaluated on a field-test dataset comprising 100,000 microscope images of unseen, real-world water samples collected from fourteen different water sources across Sammamish, WA. Precision-recall analysis showed the DeepScope model achieves 93\% accuracy, with precision of 90\% and recall exceeding 94\%. The DeepScope model was deployed on a web server, and mobile applications for Android and iOS were developed, enabling Internet-based or smartphone-based water safety testing, with results obtained in seconds.
Abstract (translated)
受粪便污染的水会导致疾病甚至死亡。当前用于微生物水质安全测试的方法需要病原体培养，耗时24至72小时，并且每次检测的成本在20至50美元之间。本文介绍了一种解决方案（DeepScope），该方案超过了联合国儿童基金会理想的产品性能指标要求，能够进行存在与否的测试，并且估计每项测试的成本为0.44美元。通过消除对病原体培养的需求，DeepScope将检测时间减少了超过98%。 在DeepScope中，一个包含细菌和水样显微镜图像的数据集被构建起来。开发了一种创新的增强技术，可以从单个显微镜图像生成多达21万亿张新图像。使用迁移学习和正则化技术开发了四个卷积神经网络模型，并通过一个现场测试数据集进行了评估，该数据集包括从华盛顿州萨马米什市十四处不同水源收集到的十万张显微镜图像。 精确度-召回率分析表明，DeepScope模型能够实现93%的整体准确率、90%的精确度和超过94%的召回率。DeepScope模型部署在了网络服务器上，并为Android和iOS开发了移动应用，实现了基于互联网或智能手机的水质安全检测，能够在几秒钟内获得测试结果。
URL
https://arxiv.org/abs/2603.06611
