In this paper, the performance of deep learning model for GoogleNet and AlexNet are analysed to classify plasmodium knowlesi life cycle stages. Plasmodium knowlesi images are taken from department of microbiology and parasitology in Hospital Universiti Sains Malaysia (HUSM) in this research work. The data images are enhanced using contrast stretching method. The enhanced image undergoes process of segmentation to extract parasite inside the effected red blood cells. The segmented images go through bounding box process according to their size input image for both deep learning models. There are 5940 data which it represents for four classes: artifact, trophozoite, schizont and gametocyte stage. These datasets are trained using GoogleNet and AlexNet to classify the life cycle stages of plasmodium knowlesi. The analysed performance of both models includes training, validation, and testing process. According to the result, both model able to reach 100% for training accuracy. For validation accuracy, AlexNet has higher accuracy with 93.4% compared to GoogleNet with 92.2%. For testing accuracy, Google has higher accuracy with 91.1% where AlexNet with 89.1%.
