Review of malaria detection using deep learning models: ResNet50, EfficientNetB3, and VGG16
Anita Kumari, Nikita Rani, Raja Ranjan, Ravikant Nirala · 2025
Although actively under research, malaria remains a very significant public health issue, particularly in tropical and subtropical countries. Traditional diagnostic techniques such as microscopes and rapid diagnostic tests (RDTs) are examples of techniques that suffer from the drawbacks of being unprecise, having long turnaround time, and also depending on highly qualified personnel. Several deep learning-based techniques have been shown to serve as an automated and precise malaria screening tool. This article analyzes the use of three trend CNN architectures, namely, ResNet50, EfficientNetB3, and VGG16 for malaria detection with focus on their advantages and disadvantages and performance indicators.