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

Abstract

One of the major health problems that modern humans encounter is malaria, which affects people of all ages. Malaria is a fatal disease caused by parasites carried by the infected mosquitoes. One way for diagnosing malaria is to examine a sample of the person's blood underneath a microscope for the presence of parasites. The project involves the creation of a web app that employs deep learning to recognize malaria parasites in images from blood smears. This can be accomplished by collecting and labeling a dataset of blood smear images utilizing convolutional neural network (CNN) models such as ResNet50, VGG19, and Customized CNN to discover patterns and features in the images. A Convolutional Neural Network (CNN) model is customized by including convolutional layers, max-pooling layers, totally connected layers, and a SoftMax layer. This approach has the power to increase the detection speed, precision of parasite diagnosis and assist in lowering the disease's global health impact.