  IPIs caused by protozoan and helminth parasites are among the most common
infections in humans in LMICs. They are regarded as a severe public health
concern, as they cause a wide array of potentially detrimental health
conditions. Researchers have been developing pattern recognition techniques for
the automatic identification of parasite eggs in microscopic images. Existing
solutions still need improvements to reduce diagnostic errors and generate
fast, efficient, and accurate results. Our paper addresses this and proposes a
multi-modal learning detector to localize parasitic eggs and categorize them
into 11 categories. The experiments were conducted on the novel
Chula-ParasiteEgg-11 dataset that was used to train both EfficientDet model
with EfficientNet-v2 backbone and EfficientNet-B7+SVM. The dataset has 11,000
microscopic training images from 11 categories. Our results show robust
performance with an accuracy of 92%, and an F1 score of 93%. Additionally, the
IOU distribution illustrates the high localization capability of the detector.
