Fast and accurate automated intestinal parasites egg detection and classification from images based on YOLOv5 deep convolutional neural network

Computer vision based on deep learning has made great stride in resent time. Several deep convolutional neural network based architectures have been built since the first release of the Alexnet model. Meanwhile, very little research on parasitology, specifically intestinal parasites, is being conducted using modern deep learning architecture based on convolutional neural networks. The goal of this research is to evaluate the performance of YOLOv5's state-of-the-art deep-learning architecture for detecting and classifying intestinal parasite eggs from images. We would ensure that patients receive prompt treatment while also relieving experts of extra work if we used such an architecture. Here, in stage first, we applied image pre-processing and augmentation to the dataset, and in stage second, we utilized the YOLOv5 algorithms for detection and classification and then compared their performance based on different parameters. Our algorithms achieved a mean average precision of 97% and 8.5 ms detection time per sample for 2,393 intestinal parasite images. Thus, this approach may form a solid theoretical basis for real-time detection and classification in routine clinical examinations while accelerating the process to satisfy increasing demand.