Classification of Parasite Malaria in Schizon Stage with GoogleNet and VGG-19 Pre-Trained Models

The development of artificial intelligence technology has currently given benefit for humans in various fields. In the medical field, artificial intelligence was developed to help medical experts to classify various diseases using medical images, including malaria. Early detection of malaria parasites is important to save the patients, thus this study developed a detection system for some malaria parasites (P. falciparum, P. vivax, and P. malariae) in the schizont stage. This system uses deep learning methods using GoogleNet and VGG 19 pre-trained models. This study performs accuracy, running time, and analysis based on the confusion matrix for testing result. The best training result is performed by the GoogleNet pre-trained model, with an average running time of 7 minutes 14 seconds and an average accuracy of 98.53% $\pm 1.27\%$. The best model for classifying malaria image in the blood is the GoogleNet model with an accuracy value of 97.41%, precision 100%, recall 93.75%, specificity 100% and f-score 99.53%.