Director, Biomedical Engineering Institute, Chiang Mai University, Chiang Mai, Thailand.

Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand.

Biography:

Nipon Theera-Umpon received his B.Eng. (Hons.) degree from Chiang Mai University, Thailand, M.S. degree from University of Southern California, U.S.A., and Ph.D. degree from the University of Missouri-Columbia, U.S.A., all in electrical engineering. Since 1993, he has been working with the Department of Electrical Engineering, Chiang Mai University, where he is now a full-professor. He is currently serving as the director of Biomedical Engineering Institute, Chiang Mai University. He has served as editor, reviewer, general chair, technical chair and committee member for several journals and conferences. He has been bestowed several royal decorations and won several awards. He was associate dean of Engineering, chairman for graduate study in electrical engineering, and chairman for graduate study in biomedical engineering. He is a member of Thai Robotics Society, Biomedical Engineering Society of Thailand, Council of Engineers in Thailand, and Engineering Institute of Thailand. He has served as Vice President of the Thailand Health Technology Association and the Thai Engineering in Medicine and Biology Society. Dr. Theera-Umpon is a senior member of the Institute of Electrical and Electronics Engineers (IEEE) and is a life member of the Asia-Pacific Signal and Information Processing Association (APSIPA) in which he is the founding chair of APSIPA Thailand Chapter. He has published more than 240 full research papers in international refereed publications and a handful of them in national publications. His textbooks in Thai language include Digital Signal and Image Processing: Theories and Applications, Advanced Digital Signal Processing, Digital Signal Processing in Telecommunications, etc. Whereas the textbook “Digital Signal and Image Processing: Theories and Applications” received The Outstanding Engineering Textbook Award 2022 from The Fund Management Committee for Education and Research in Engineering Under The Royal Patronage of His Royal Highness Crown Prince Maha Vajiralongkorn, Engineering Institute of Thailand Under H.M. The King’s Patronage. His research interests include Pattern Recognition, Digital Image Processing, Artificial Intelligence, Neural Networks, Fuzzy Sets and Systems, Machine Learning, Big Data Analysis, Data Mining, Medical Signal and Image Processing.

Automatic Malaria Diagnosis in Blood Smear Images

     Malaria is a leading cause of morbidity and mortality in tropical and sub-tropical regions. This research proposed a malaria diagnosis system based on the you only look once algorithm for malaria parasite detection and the convolutional neural network algorithm for malaria parasite life stage classification. Two public datasets are utilized: MBB and MP-IDB. The MBB dataset includes human blood smears infected with Plasmodium vivax (P. vivax). While the MP-IDB dataset comprises 4 species of malaria parasites: P. vivax, P. ovale, P. malariae, and P. falciparum. Four distinct stages of life exist in every species, including ring, trophozoite, schizont, and gametocyte. For the MBB dataset, detection and classification accuracies of 0.92 and 0.93, respectively, were achieved. For the MP-IDB dataset, the proposed algorithms yielded the accuracies for detection and classification as follows: 0.84 and 0.94 for P. vivax; 0.82 and 0.93 for P. ovale; 0.79 and 0.93 for P. malariae; and 0.92 and 0.96 for P. falciparum. The detection results showed the models trained by P. vivax alone provide good detection capabilities also for other species of malaria parasites. The classification performance showed the proposed algorithms yielded good malaria parasite life stage classification performance.