Comparison of Models based on convolutional neural networks: ResNet-50V2, MobileNetV2 and EfficientNetB0 in the detection of Malaria
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Abstract
Malaria remains one of the leading causes of mortality worldwide, disproportionately affecting the most vulnerable populations. Traditional malaria diagnosis, based on manual microscopy, is prone to human errors and is time-consuming, hindering timely detection. This study compares three convolutional neural network (CNN) models: ResNet-50V2, MobileNetV2, and EfficientNetB0, applied to the automatic detection of malaria-infected cells. Using a publicly available dataset of blood cell images, metrics such as precision, recall, F1-score, and accuracy were evaluated. The results show that EfficientNetB0 achieved the best performance, with a precision of 97.12% and a recall of 97.59%, outperforming ResNet-50V2 and MobileNetV2 in overall performance. While ResNet-50V2 presented comparable results, MobileNetV2, though less accurate, stood out for its computational efficiency, making it suitable for devices with limited resources. The findings suggest that model selection should depend on the balance between accuracy and available computational resources, with EfficientNetB0 being the most appropriate for automated medical diagnostic systems in environments with higher processing capacity, while MobileNetV2 is ideal for resource-constrained environments.
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