An Efficient VGG19 Framework for Malaria Detection in Blood Cell Images

Malaria diagnosis by microscopy is a method for identifying malaria using cell pictures. In order to do this, a blood sample must be examined under a microscope to determine whether red blood cells contain the malaria parasite. Computer vision method is used to examine the photos and determine if the malaria parasite species are present in order to automate this procedure utilising cell images. Large datasets of cell pictures are used to train machine learning algorithms to spot patterns and traits that indicate a malaria infection. In this work, we aim to develop a sustainable picture classification model based on exchange learning that can identify malaria using cell pictures. Our VGG19 model was shown to have good classification performance for identifying malaria, with an accuracy rate of more than 90% which shall improve ecosystem management.