Background: Manual microscopic examination remains the golden standard for malaria diagnosis. However, it is cumbersome and requires the experience of pathologists for accurate diagnosis. The shear workload and challenges involved in manual microscopy drives for alternative computer-aided diagnosis techniques. While the importance of computer-aided diagnosis is increasing at an enormous pace fostered by the advancement of deep learning algorithms, there are still challenges. Existing state-of-the-art (SOTA) deep learning-based object detection models suffer from effectively detecting small objects which are less represented on benchmark datasets and are affected by the loss of detailed spatial information due to in-network feature map downscaling. The problem even becomes harder when the input image is high resolution. This is due to the fact that existing SOTA models will not directly process high-resolution images limited by their low-resolution network input size.
Methods: In this study, an effective and robust tile-based image processing approach was proposed to enhance malaria parasite detection performance of SOTA object detection models. Three YOLOV4 based object detectors were adopted considering their detection speed and accuracy. The proposed detection models were developed by using tiles generated from 1,780 high-resolution P. falciparum-infected thick smear microscopic images. External validation was performed to verify the detection accuracy and generalization ability of the proposed models on three datasets acquired from a different domain.
Results: The best-performing model leveraging the proposed tile-based approach significantly outperforms its baseline method (Recall, [95.3%] vs [57%] and Average Precision, [87.1%] vs [76%]). In addition, the proposed method achieved state-of-the-art performance compared with previous research works which use different machine learning techniques on similar datasets.
Conclusions: The obtained qualitative and empirical results show that the proposed method reveals a fundamental performance improvement for the detection of P. falciparum from thick smear microscopic images while maintaining real-time speed. Furthermore, the proposed method could have the potential to assist and reduce the workload of laboratory technicians in malaria-endemic remote areas in developing countries where there is a critical skill gap and shortage of experts.