MALARIA DIAGNOSIS BY TRANSFER LEARNING ANALYSIS OF PARASITIZED AND UNINFECTED RED BLOOD CELL IMAGES USING VGG16, DENSENET201, VGG21, AND VGG19

Malaria is still a major worldwide health concern, and effective treatment depends on early discovery. Using well-known deep learning models like VGG16, DenseNet201, VGG21, and VGG19, this study focuses on applying transfer learning techniques to diagnose malaria by examining photos of red blood cells. To identify malaria-infected cells, the dataset's red blood cell images—both parasitized and uninfected—are processed. With the intention of offering a more dependable and approachable malaria diagnostic technique, the objective is to assess the effectiveness of these predictive models in terms of accuracy, precision, and computational performance. This strategy ensures improved performance. Different methods were tried, and all classifiers' outputs were combined using an ensemble technique. The suggested technique outperformed the individual machine learning classifiers and ensemble methods employed in this study in terms of accuracy, precision, and sensitivity, with rates of 96.94%, 96.94%, and 96.94%. Furthermore, the association between the factors was investigated, showing each factor's contribution to malaria occurrence. The findings show that the suggested approach may efficiently anticipate malaria outbreaks.