Automated intestinal parasite detection in stool samples using custom convolutional neural networks

Abstract

Intestinal parasitic infections can cause serious health problems with relatively high infections in the developing world. Microscopy of stool remains the gold standard method for the diagnosis of intestinal parasites. However, this method can be time-consuming, and it is also challenging to maintain consistency in diagnosis across different technicians. This is also hindered by the few competent and skilled technicians in the developing countries where the prevalence of intestinal parasites is high. Deep learning has increasingly gained application ground in different challenging computer vision tasks. There is also growing literature of the use of the same technologies in health diagnostic fields such as microscopy. What is used in the state-of-art computer vision challenges, oftentimes gets applied to real-world challenges. However, this has met different limitations in sensitivity and specificity given the broader range of diversity in data sets; for example, in this study of intestinal parasite detection. In general, deep learning continues to provide good performance to computer vision problems across multiple disciplines. In this work, the use of AlexNet and GoogleNet models’ performance on the diagnosis of intestinal parasite eggs in stool samples is evaluated. This work goes ahead to compare these out-of-the-box fine-tuned models with a custom-trained Convolutional Neural Network on the same task. In all cases, accuracy from the out-of-the-box models is very high with GoogleNet ROC AUC of 0.99 and AlexNet ROC AUC of 1.00, and runs on a very low computing resource system, which speaks to the fact that out-of-box models can re-purposed for real-world health diagnostic challenges.