ParasNet: Fast Parasites Detection with Neural Networks
This work addresses the need for real-time, high-accuracy detection of waterborne parasites, which is crucial for public health, though it is incremental as it applies deep learning to an under-explored medical application.
The paper tackled the problem of detecting Cryptosporidium and Giardia parasites in drinking water at the cell level using bright-field imaging, achieving over 97% accuracy and 700+ fps speed on an embedded platform.
Deep learning has dramatically improved the performance in many application areas such as image classification, object detection, speech recognition, drug discovery and etc since 2012. Where deep learning algorithms promise to discover the intricate hidden information inside the data by leveraging the large dataset, advanced model and computing power. Although deep learning techniques show medical expert level performance in a lot of medical applications, but some of the applications are still not explored or under explored due to the variation of the species. In this work, we studied the bright field based cell level Cryptosporidium and Giardia detection in the drink water with deep learning. Our experimental demonstrates that the new developed deep learning-based algorithm surpassed the handcrafted SVM based algorithm with above 97 percentage in accuracy and 700+fps in speed on embedded Jetson TX2 platform. Our research will lead to real-time and high accuracy label-free cell level Cryptosporidium and Giardia detection system in the future.