Parasite Detection in Thick Blood Smears Based on Customized Faster-RCNN on Smartphones

Abstract

Malaria is a worldwide life-threatening disease. The gold standard for malaria diagnosis is microscopy examination, which includes thick blood smears to detect the presence of parasites and thin blood smears to differentiate the development stages of parasites. Microscopy examination is of low cost but is time consuming and error-prone. Therefore, the development of an automated parasite detection system for malaria diagnosis in thick blood smears is an important research goal, especially in resource-limited areas. In this paper, based on a customized Faster-RCNN model, we develop a machine-learning system that can automatically detect parasites in thick blood smear images on smartphones. To make Faster-RCNN more efficient for small object detection, we split an input image of $4032 \times 3024 \times3$ pixels into small blocks of $252 \times 189 \times3$ pixels, and then train the FasterRCNN model with the small blocks and corresponding parasite annotations. Moreover, we customize the convolutional layers of Faster-RCNN with four convolutional layers and two maxpooling layers to extract features according to the input image size and characteristics. We perform experiments on 2967 thick blood smear images from 200 patients, including 1819 images from 150 patients who are infected with parasites. The customized FasterRCNN model is first trained on small image blocks from 120 patients, including 90 infected patients and 30 normal patients, and then tested on the remaining 80 patients. For testing, we also split each input image into small blocks of $252 \times 189 \times3$ pixels that are screened by our trained Faster-RCNN model to detect parasite coordinates, which are then re-projected into the original image space. Detection rates of our system on image level and patient level are 96.84% and 96.81%, respectively.