Localization and Classification of Parasitic Eggs in Microscopic Images Using an EfficientDet Detector
This work addresses a public health concern in low- and middle-income countries by improving diagnostic accuracy for parasitic infections, though it is incremental as it applies an existing method to a new dataset.
The paper tackled the problem of automatically identifying parasitic eggs in microscopic images for diagnosing intestinal parasitic infections, achieving robust performance with 92% accuracy and 93% F1 score.
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.