Field Screening of Thin Blood Smears for Plasmodium falciparum Parasites Using the Coral TPU

Abstract

Accurate and rapid detection of Plasmodium falciparum parasites in blood smears is critical for the timely diagnosis and treatment of malaria, particularly in resource-constrained field settings. This paper presents a proof-of-concept solution demonstrating the feasibility of the Google Coral Edge Tensor Processing Unit (TPU) for real-time screening of thin blood smears for P. falciparum infection. We develop and deploy a lightweight deep learning model optimized for edge inference using transfer learning and training data supplied by the NIH. This model is capable of detecting individual parasitized red blood cells (RBCs) with high sensitivity and specificity. In a final deployment, the system will integrate a portable digital microscope and low-power color display with the Coral TPU to perform on-site image capture and classification without reliance on cloud connectivity. We detail the model training process using a curated dataset of annotated smear images, potential future hardware integration for field deployment, and performance benchmarks. Initial tests show that the Coral TPU-based solution achieves an accuracy of 92% in detecting P. falciparum parasites in thin-smear microscopy images, with processing times under 50 ms per identified RBC. This work illustrates the potential of edge AI devices to transform malaria diagnostics in low-resource settings through efficient, affordable, and scalable screening tools.