Parasitic infections are one of the leading causes of deaths and other ailments worldwide. Detecting such infections using traditional diagnostic procedures requires experienced medical technologists together with a significant amount of time and effort. An automated procedure with the ability to accurately detect parasitic diseases can greatly accelerate the process. This work proposes a deep learning-based object detection for parasitic egg detection and classification. We show that multitask learning via pseudo-mask generation improves the single model performance. Moreover, we show that a combination of multitask learning, pseudo-label generation, and ensembling model predictions can accurately detect parasitic egg cells. Continuous training via pseudo-label generation and ensemble predictions improves the accuracy of single-model detection. Our final model achieved a mean precision score (mAP) of 0.956 on a validation set of 1,650 images. Our best model obtained mIoU and mF1 scores of 0.934 and 0.988 respectively. We discuss its technical implementation in this paper.

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Background: Artificial intelligence (AI) has gained increasing popularity in human society, and it is important to educate people about this emerging technology. Many countries have adopted school curricula to incorporate AI into their classrooms.However, developing tools for discovering AI concepts remains challenging. There are few studies on AI education tools, particularly in Thailand.Objectives: This study designs AIThaiGen, a web-based learning platform for junior high school students that introduces AI concepts. It can communicate with remote hardware stations, allowing students to test their AI models in real-world scenarios.Methods: A total of 106 students in 7th and 8th grade in Thailand participated, and a single-group pre-test-post-test research design was employed in this study. Prepost-tests on the basic concepts of AI and the students' attitude questionnaire on AIThaiGen were used to collect and analyse data. Results and Conclusions:The results show that there is a significant improvement (p < 0.001) in the pre-post-tests on the basic concepts of AI, and the overall result of the students' attitude questionnaire on AIThaiGen is X ¼ 3:88, indicating positive outlooks. Furthermore, notable student projects are showcased, highlighting their ability to initiate new ideas for solving real problems after studying with AIThaiGen.

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