Automated Classification of Multi-Class Human Protozoan Parasites using Xception as Transfer Learning
Keywords:
Protozoan, Parasites, Transfer Learning, Xception OptimizerAbstract
Infections caused by protozoan parasites can cause serious health problems. Malaria infection is an infection with the most recorded infection cases in the world as reported by World Health Organizer (WHO) which has reported cases of Malaria infection in 2020 experienced a significant increase from the previous year. There were 241 million global cases, which previously recorded 227 million global cases. Apart from Malaria, there are still many cases caused by other protozoan parasites such as Babesia, Leishmania, Toxoplasma, Trichomonad, and Trypanosome who identified in this study. Therefore, it is necessary to use technology that can help microscopic experts to diagnose in a fast time and with optimal results. Using the Transfer Learning method with Xception architecture can produce optimal accuracy. In the research in this paper, we use four different optimizers including Adam, RMSprop, SGD, and Adadelta as comparisons with each other. Of the four optimizers, the most optimal result in this study is Adam optimizer with an accuracy of 97%. Therefore, the methodology that we use can help classify the types of protozoan parasites which can make the process faster and can reduce costs in the identification process.
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