A Web-Based Approach for Malaria Parasite Detection Using Deep Learning in Blood Smears

Malaria affects public health issues significantly and is one of the most severe infectious diseases in the world. Anopheles mosquitoes attack humans who carry the virus in order to disseminate it. To manage the sickness and get the best potential treatment outcomes, accurate parasite identification is essential. A critical first step in the diagnosis and treatment of malaria is the traditional method of using a microscope to search blood samples for malarial parasites. A diagnosis made using this approach is time-consuming since it relies heavily on the examiner's expertise and experience. To improve the speed and accuracy of diagnosis, this study suggests a deep learning model for malarial parasite prediction.In this study, we report on a Convolutional Neural Networks (CNN) model, also called the VGG-19 model, which detects malaria parasites with 97% accuracy using microscopic images of blood samples. Enhancing the efficacy and precision of the diagnosis is the aim of this method. This model has been trained on a set of images of blood smears and is capable of accurately distinguishing between samples that are infected and those that are not. Malaria may be less common in areas where it is endemic if this automated diagnostic method is successfully implemented and results in early diagnosis and treatment.