Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

The goal of the research is to develop an artificial intelligence AI model that makes use of a hybrid deep learning technique of object detection and classification backbones for defining the protozoa trypanosome specie, namely trypanosoma cruzi, T.brucei, and T.evansi, from oil immersion, microscopic images on the in-house, low-code AI platform CiRA CORE. The most recent development in our field of research is the development of the learning algorithms that can identify and classify trypanosome species from microscopic images. This program has the potential to revolutionize the surveillance and control by providing a rapid automated and accurate screening method that can be used by local staff in remote areas.

Using the modified and hybrid algorithms between two different deep learning models within the purpose AI program can overcome many challenges. Identification of shared morphology, mixed and immature infection, and precise species characterization enables automatic standard taxonomy.