PARASITIC EGG DETECTION AND CLASSIFICATION WITH TRANSFORMER-BASED ARCHITECTURES

Soil-transmitted helminth infections are one of the most common healthcare problems worldwide and they especially affect to the poorest communities in tropical and subtropical areas. Nowadays, diagnosis of intestinal parasites is performed by highly skilled medical staff, directly examining samples in the laboratory via a microscope, a laborious and time-consuming work. Automatic deep learning-based object detection methods can help to automatically detect and identify intestinal parasitic eggs, or at least reduce the workload. In this work, the application of novel Transformer-based architectures is proposed to solve the parasitic egg detection task in microscopic images. Several detection methods and backbones have been analyzed and compared, obtaining up to 0.875 mIoU score on the dataset used for testing.