Rooney Adu-Gyamfi
Malaria is a serious mosquito-borne disease caused by the plasmodium parasite. It is transmitted when an infected mosquito bites a person. Early diagnosis of malaria is critical in the diagnosis of malaria before it multiplies and leads to death. The golden standard for diagnosis of malaria is the microscopic examination. The major drawback of this approach is that, it is time consuming and labour intensive. According to the WHO`s 2020 World malaria report, there was an estimated 241 malaria cases and 627,000 deaths worldwide in 2020 . About 47,000 of these cases were linked to improper diagnosis and disruptions. A study in Ghana found that there were only 1.72 microscopes per 100,000 population but only 0.85 trained technicians per 100,000 population . Due to this unavailability of microscopes and skill technicians, diagnosis are made on the basis of clinical signs and symptoms alone. The purpose of this study is to determine the effectiveness of convolutional neural networks in categorizing microscopic blood smear images as parasitized or healthy. This research aims to explore how the recent advancements in Artificial intelligence and Deep learning can be used to automate the diagnosis of malaria parasite. Two deep learning models VGG-19 and ResNet-50 will be largely examined and compared to determine the bet performing model on a malaria dataset.