Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning ModelVeerayuth Kittichai 1, Morakot Kaewthamasorn 2, Suchansa Thanee 2, Thanyathep Sasisaowapak 3, Kaung Myat Naing 3, Rangsan Jomtarak 4, Teerawat Tongloy 3, Santhad Chuwongin 3, Siridech Boonsang 5
1Faculty of Medicine, King Mongkut’s Institute of Technology Ladkrabang, 2Veterinary Parasitology Research Unit, Faculty of Veterinary Science, Chulalongkorn University, 3College of Advanced Manufacturing Innovation, King Mongkut’s Institute of Technology Ladkrabang, 4Faculty of Science and Technology, Suan Dusit University, 5Department of Electrical Engineering, School of Engineering, King Mongkut’s Institute of Technology Ladkrabang
Worldwide medical blood parasites were automatically screened using simple steps on a low-code AI platform. The prospective diagnosis of blood films was improved by using an object detection and classification method in a hybrid deep learning model. The collaboration of active monitoring and well-trained models helps to identify hotspots of trypanosome transmission.
