Web-Based Malaria Diagnosis using Deep Convolutional Neural Networks for Granular Blood Sample Analysis
Keywords:
Malaria Disease, Red Blood Cells (RBC), Deep Learning, Convolutional Neural network (CNN)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, max-pooling 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.
