Comparison of EfficientNetB0 and EfficientNetB7 Models in Classifying Malaria Based on Blood Cells

Abstrak

Malaria is a disease caused by the bite of malaria mosquitoes, which spreads through blood. Malaria mosquitoes will spread the Plasmodium parasite through their bites. Early malaria identification is essential so the disease can be prevented immediately. Through data science, which utilizes the CNN model, the classification of blood infected with parasites can be predicted accurately. This research uses data obtained from Kaggle website with 27,558 image samples. The data is divided into two classes, parasite-infected and uninfected, which are then divided again into two types. The first class is training data divided into 80% of the total data and the other 20% as validation data. This research used two test scenarios to obtain a more effective classification model. The first scenario uses Hyperparameter Tuning and the EfficientNetB0 model with classification results of 95%. Meanwhile, the classification achievement for scenario two was 99% by utilizing EfficientNetB7.