Enhanced detection of malaria infected red blood cells through phase driven classification

Digital holographic microscopy (DHM) has emerged as a powerful, label-free technique for visualizing and analyzing biological samples. By extracting the intrinsic optical properties of red blood cells (RBCs), DHM enables the detection of infection-induced morphological and biophysical changes. Traditional classification methods often rely on feature-specific analysis, which can lead to misclassification when a single parameter fails to differentiate between uninfected and infected cells. In this study, we present a novel features-based approach that integrates multiple features to classify Plasmodium falciparum-infected RBCs obtained using lensless inline DHM. Our analysis shows that phase-based features classification provides a more reliable indicator of infected RBCs compared to morphological features. Additionally, our features-based approach outperforms feature-specific methods that rely on individual attributes. The parasitemia detection rate improved from 48% (feature-specific method) to 61% (phase-based features method) on the same sample set, demonstrating enhanced detection accuracy. Furthermore, the proposed method achieved high specificity (98-100%), ensuring reliable identification of uninfected cells. Although our method slightly underestimates the parasitemia detection rate compared to Giemsa staining (90%), it offers a significant advantage as a real-time, label-free imaging tool, presenting a promising avenue for rapid and automated malaria diagnosis.