Analysis of the VGG19 Model for Malaria Parasite Detection
Abstract
Malaria is transmitted by female anopheles mosquitoes harboring the plasmodium bacteria. The conventional diagnosis of malaria infection involves an expert's manual detection of parasitized cells. The accuracy of detection depends upon the quality of thick blood smears and the expertise of a trained microscopist in counting and classifying the infected and uninfected cells in those blood smears. This approach is error-prone and time- consuming. Computer-aided diagnostic methods have proven to be more accurate than traditional approaches. This paper analyses the VGG19 model for the automated detection of malaria parasites. The testing and validation accuracy of the VGG19 model is found to be 89.40% and 89.90%, respectively.
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