Parasitized Cell Recognition Using AlexNet Pre-trained Model

Malaria is globally known as one of the most prevalent diseases that kill thousands of people every year. Plasmodium parasites are the product of malaria disease that infects the red blood cells of humans. These parasites are transmitted by a female mosquito class that is known as anopheles. The diagnostic process of malaria involves isolation and manual counts in microscopic bloodstreams of parasitized cells by medical practitioners. In large-scale screening, Malaria diagnostic accuracy is largely affected because of resource unavailability. In this paper, we proposed an intelligent diagnosis system using advanced techniques based on a deep learning algorithm precisely AlexNet pre-trained model. As the bright side of machine learning techniques, CNN has greatly led to numerous image recognition activities. This method shows encouraging results. In terms of accuracy, the proposed model achieved 97.33% in the validation phase. Therefore, in some places where there are no medical services, this approach can be widely used for diagnosing parasitized cells.