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

Veerayuth Kittichai; Morakot Kaewthamasorn; Suchansa Thanee; Thanyathep Sasisaowapak; Kaung Myat Naing; Rangsan Jomtarak; Teerawat Tongloy; Santhad Chuwongin; Siridech Boonsang

doi:10.3791/65557

AI Program for Trypanosomiasis Detection and Classification

October 27, 2023

AI-Driven Trypanosome Species Detection from Microscopic Images (Video) | JoVE

01:29 min

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Training Process CiRA CORE Platform and Object Detection Model Evaluation to Identify and Classify the Protozoan Trypanosome Species (Video) | JoVE

04:00 min

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Image Classification as Model Training and Model Evaluation to Identify and Classify the Protozoan Trypanosome Species (Video) | JoVE

01:37 min

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Hybrid (Detection and Classification) as Model Testing to Identify and Classify the Protozoan Trypanosome Species (Video) | JoVE

01:26 min

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Veerayuth Kittichai, Morakot Kaewthamasorn, Suchansa Thanee, Thanyathep Sasisaowapak, Kaung Myat Naing, Rangsan Jomtarak, Teerawat Tongloy, Santhad Chuwongin, Siridech Boonsang

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

Summary

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.