Classification and Detection of Malaria from Parasitized and Uninfected Red Blood Cell Images Using Transfer Learning Based Ensemble Model

Malaria is a potentially lethal infectious disease caused by the Plasmodium parasite. The transmission of this disease to humans occurs via the bites of Anopheles mosquitoes that are infected with the pathogen. The impact of this disease on the health systems of vulnerable nations, especially in sub-Saharan Africa, is profound and catastrophic. Malaria infiltrates and reproduces within red blood cells, leading to their destruction and the release of harmful substances into the circulation. The parasite’s capacity to adhere to and alter the surface of red blood cells might induce their adhesiveness, impeding blood circulation in crucial organs including the brain and spleen. Hence, it is crucial to employ effective methods for promptly identifying malaria in order to preserve patients’ lives. The primary objective of this project is to establish a very effective model for the early detection of malaria. For the study trials, we utilized malaria pictures depicting both parasitized and uninfected red blood cells. We employed a transfer learning ensemble model, utilizing three distinct pretrained models: VGG16, Resnet-50, and Inception-V3. The models were trained with softmax activation, Adam optimizer with a learning rate of 0.002, categorical-crossentropy loss function, and accuracy matrices. Ultimately, in order to get an improved outcome, we combine all three models and obtain an accuracy rate of 98.6%. We evaluate our model using data that was not used throughout the training and validation procedure.