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.
From a public health perspective, this opinion article discusses the necessity to push for telehealth in the Philippines as a mode of healthcare delivery, based on lessons from other Southeast Asian countries. With the recent pandemic, the Philippines has witnessed the potential of telehealth to cater to the healthcare needs of the public. Telehealth fills in the gaps brought about by the pandemic, delivering quality healthcare services to Filipinos. We hope that this encourages both the public and private sectors to lend their full support to efforts to promote telehealth, particularly in the Philippines.
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