Abstract
Plasmodium vivax is a malaria parasite with a broad geographic distribution worldwide. The unique biological characteristics of P. vivax, such as early gametocytogenesis and its latent hypnozoite stage, make it more difficult to control compared to P. falciparum. Malaria remains a significant global health concern, particularly in regions with limited diagnostic infrastructure. This study aims to develop a computer-assisted method for characterizing and classifying malaria parasites using a machine learning approach based on light microscopic images of peripheral blood smears. One of the major challenges in malaria diagnostics is the inadequacy of current detection methods. To address this, the study introduces a convolutional neural network (CNN)-based pipeline for the automated detection and staging of malaria infections from Giemsa-stained blood smear images. The dataset used in this study was annotated into four classes: Ring Form, Trophozoite, Schizont, and Uninfected Red Blood Cells (RBCs), encompassing diverse staining qualities and morphological variations. The dataset was divided into training (70%), validation (15%), and testing (15%) subsets. The CNN achieved an overall classification accuracy of 92.4%, with precision, recall, and F1-scores exceeding 0.90 across all classes. Statistical metrics, including mean accuracy (92.4% ± 2.1%), precision (93.1% ± 1.8%), and recall (92.8% ± 1.9%), demonstrated the robustness of the model. Class-specific analysis revealed that the Schizont stage achieved the highest classification accuracy (94.7%), while the Ring Form stage showed slightly lower performance (91.2%), likely due to inherent morphological overlaps with early Trophozoite forms. Visualizations, including confusion matrices and class probability distribution overlays, provided detailed insights into the model's decision-making processes. The pipeline was further evaluated using cross-validation techniques, showing high reliability across various dataset splits. This approach offers scalability and adaptability, with the potential for deployment in real-world diagnostic workflows, particularly in resource-constrained settings.