Malaria Parasite Classification from Microscopic Images using EfficientNetV2B0 with Bayesian Optimization

Abstract

The Plasmodium parasite, which spreads through the bite of the Anopheles mosquito, causes malaria, a significant global health concern. Notwithstanding attempts to curtail its proliferation, malaria continues to be a predominant cause of mortality in tropical nations, especially in Sub-Saharan Africa and certain regions of Southeast Asia. Timely identification and precise diagnosis are essential for effective treatment. This research seeks to create a malaria classification model using deep learning based on the EfficientNetV2B0 architecture. The model is engineered to identify malaria parasite infections in microscopic images of erythrocytes. The dataset used is an open-source collection of photographs depicting red blood cells categorised as either infected or uninfected with malaria. The development method encompasses multiple critical stages, beginning with data collection, followed by preprocessing, data augmentation, and modelling using transfer learning with the EfficientNetV2B0 model. Bayesian optimisation is used to improve the model's accuracy by adjusting its hyperparameters. Assessment metrics, including accuracy, precision, recall, and F1-score, are used to evaluate the trained model's performance. The results show that the model has an accuracy of 96%, with equivalent precision, recall, and F1-scores for both the infected (under the heading "Parasitised") and uninfected (under the heading "Uninfected") groups. The model is extremely effective in diagnosing malaria, making it a valuable diagnostic tool for malaria control and prevention, especially in resource-constrained locations.

Malaria Parasite Classification from Microscopic Images using EfficientNetV2B0 with Bayesian Optimization