Web-based Deep Learning System for Malaria Parasite Detection in Granular Blood Samples

Abstract

Abstract: Malaria is a serious health concern for modern humans, affecting people of all ages. Infected mosquitoes carry the fatal parasites responsible for malaria. Malaria can be diagnosed by examining a sample of the patient's blood under a microscope for parasites. The project comprises creating a web tool that employs deep learning to detect malaria parasites in blood smear photos. Convolutional neural network (CNN) models such as ResNet50, VGG19, and Customized CNN can be used to collect and categorize a set of blood smear images in order to identify patterns and characteristics. Convolutional layers, maxpooling layers, entirely linked layers, and a SoftMax layer are all utilized to create a Convolutional Neural Network (CNN) model. This technique can improve the accuracy of parasite diagnosis, increase detection rates, and reduce the disease's impact on global health.