  According to the World Malaria Report of 2022, 247 million cases of malaria
and 619,000 related deaths were reported in 2021. This highlights the
predominance of the disease, especially in the tropical and sub-tropical
regions of Africa, parts of South-east Asia, Central and Southern America.
Malaria is caused due to the Plasmodium parasite which is circulated through
the bites of the female Anopheles mosquito. Hence, the detection of the
parasite in human blood smears could confirm malarial infestation. Since the
manual identification of Plasmodium is a lengthy and time-consuming task
subject to variability in accuracy, we propose an automated, computer-aided
diagnostic method to classify malarial thin smear blood cell images as
parasitized and uninfected by using the ResNet50 Deep Neural Network. In this
paper, we have used the pre-trained ResNet50 model on the open-access database
provided by the National Library of Medicine's Lister Hill National Center for
Biomedical Communication for 150 epochs. The results obtained showed accuracy,
precision, and recall values of 98.75%, 99.3% and 99.5% on the
ResNet50(proposed) model. We have compared these metrics with similar models
such as VGG16, Watershed Segmentation and Random Forest, which showed better
performance than traditional techniques as well.
