MALARIA DETECTION USING DEEP LEARNING

Abstract

Every year, hundreds of millions of people throughout the world succumb to the fatal disease known as malaria. If the treatment is not given in a timely manner, disaster may result. The microscopic method is still the most used, despite recent improvements in malaria detection. The speed at which malaria diagnoses may be made is, however, constrained by the precision of microscopic diagnostics, which is reliant on the expertise of the microscopes. Deep learning methodologies can provide a more accurate diagnosis of an illness. Comparing the Adam-based model to earlier models created for recognizing contaminated cells. In a variety of accuracy criteria, the model outperforms the majority of previously produced models. The fact that the model just has a few layers contributes to its simplicity. This uses less computer power and processing time. Moreover, we test our model on two types of datasets infected and uninfected cells.