Detection of Malaria Infections Using Convolutional Neural Networks

Abstract

Malaria persists as a serious global public health threat, particularly in resource-limited regions where timely and accurate diagnosis is a challenge due to poor medical infrastructure. This study presents a comparative evaluation of three pre-trained convolutional neural network (CNN) architectures—EfficientNetB0, InceptionV3, and ResNet50—for automated detection of Plasmodium-infected blood cells using the Malaria Cell Images Dataset. The models were implemented in Python with TensorFlow and trained in Google Colab Pro with GPU A100 acceleration. Among the models evaluated, ResNet50 proved to be the most balanced, achieving 97% accuracy, a low false positive rate (1.8%) and the shortest training time (2.9 hours), making it a suitable choice for implementation in real-time clinical settings. InceptionV3 obtained the highest sensitivity (98% recall), although with a higher false positive rate (4.0%) and a higher computational demand (6.5 hours). EfficientNetB0 was the fastest model (3.2 hours), showed validation and a higher false negative rate (6.2%). Standard metrics—accuracy, loss, recall, F1- score and confusion matrix—were applied under a non- experimental cross-sectional design, along with regularization and data augmentation techniques to improve generalization and mitigate overfitting. As a main contribution, this research provides reproducible empirical evidence to guide the selection of CNN architectures for malaria diagnosis, especially in resource- limited settings. This systematic comparison between state-of-the- art models, under a single protocol and homogeneous metrics, represents a significant novelty in the literature, guiding the selection of the most appropriate architecture. In addition, a lightweight graphical user interface (GUI) was developed that allows real-time visual testing, reinforcing its application in clinical and educational settings. The findings also suggest that these models, in particular ResNet50, could be adapted for the diagnosis of other parasitic diseases with similar cell morphology, such as leishmaniasis or babesiosis.