Deep Learning based Web App for Malaria Parasite Detection in Granular Blood Samples
K. Santoshi, G. Saranya, Ch.Rama Reddy, Ch. Jathin Reddy, K. Gyananandu, G. Nikhil Tej · 2023
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.