Unsupervised Toxoplasma gondii Recognition by Fuzzy Cycle Generative Adversarial Network

Abstract

Toxoplasma gondii, one of the worlds most common parasites, can infect all types of warm-blooded animals, including one-third of the worlds human population. Most current routine diagnostic methods are costly, time-consuming, and labor-intensive. Although T.gondii can be directly observed under the microscope in tissue or spinal fluid samples, this form of identification is difficult and requires well trained professionals. Nevertheless, the traditional identification of parasites under the microscope is still performed by a large number of laboratories. Novel, efficient and reliable methods of T.gondii identification are therefore needed, particularly in developing countries. To this end, we developed a novel transfer learning based microscopic image recognition method for T.gondii identification. This approach employs Fuzzy Cycle Generative Adversarial Network (FCGAN) with transfer learning utilizing knowledge gained by the parasitologists that Toxoplasma is in banana or crescent shaped form. Our approach aims to build connection between micro and macro associated objects by embedding fuzzy C-means cluster algorithm into Cycle Generative Adversarial Network (Cycle GAN). Our approach achieves 93.1% and 94.0% detection accuracy for 400X and 1000X Toxoplasma microscopic images respectively. We show the high accuracy and effectiveness of our approach in the newly collected unlabeled Toxoplasma microscopic images, comparing to other current available deep learning methods. This novel method for Toxoplasma microscopic image recognition will open a new window for developing cost-effective and scalable deep learning based diagnostic solution, potentially enabling broader clinical access in developing countries.