Timely and rapid diagnosis is crucial for faster and proper malaria treatment planning. Microscopic examination is the gold standard for malaria diagnosis, where hundreds of millions of blood films are examined annually. However, this method's effectiveness depends on the trained microscopist's skills. With the increasing interest in applying deep learning in malaria diagnosis, this study aims to determine the most suitable deep-learning object detection architecture and their applicability to detect and distinguish red blood cells as either malaria-infected or non-infected cells. The object detectors Yolov4, Faster R-CNN, and SSD 300 are trained with images infected by all five malaria parasites and from four stages of infection with 80/20 train and test data partition. The performance of object detectors is evaluated, and hyperparameters are optimized to select the best-performing model. The best-performing model was also assessed with an independent dataset to verify the models' ability to generalize in different domains. The results show that upon training, the Yolov4 model achieves a precision of 83%, recall of 95%, F1-score of 89%, and mean average precision of 93.87% at a threshold of 0.5. Conclusively, Yolov4 can act as an alternative in detecting the infected cells from whole thin blood smear images. Object detectors can complement a deep learning classification model in detecting infected cells since they eliminate the need to train on single-cell images and have been demonstrated to be more feasible for a different target domain.
Malaria cases caused by the zoonotic parasite Plasmodium knowlesi are being increasingly reported throughout Southeast Asia and in travelers returning from the region. To test for evidence of signatures of selection or unusual population structure in this parasite, we surveyed genome sequence diversity in 48 clinical isolates recently sampled from Malaysian Borneo and in five lines maintained in laboratory rhesus macaques after isolation in the 1960s from Peninsular Malaysia and the Philippines. Overall genomewide nucleotide diversity (π = 6.03 × 10(-3)) was much higher than has been seen in worldwide samples of either of the major endemic malaria parasite species Plasmodium falciparum and Plasmodium vivax. A remarkable substructure is revealed within P. knowlesi, consisting of two major sympatric clusters of the clinical isolates and a third cluster comprising the laboratory isolates. There was deep differentiation between the two clusters of clinical isolates [mean genomewide fixation index (FST) = 0.21, with 9,293 SNPs having fixed differences of FST = 1.0]. This differentiation showed marked heterogeneity across the genome, with mean FST values of different chromosomes ranging from 0.08 to 0.34 and with further significant variation across regions within several chromosomes. Analysis of the largest cluster (cluster 1, 38 isolates) indicated long-term population growth, with negatively skewed allele frequency distributions (genomewide average Tajima's D = -1.35). Against this background there was evidence of balancing selection on particular genes, including the circumsporozoite protein (csp) gene, which had the top Tajima's D value (1.57), and scans of haplotype homozygosity implicate several genomic regions as being under recent positive selection.