{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T03:15:44Z","timestamp":1758078944280,"version":"3.44.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686196","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T00:00:00Z","timestamp":1757980800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9,16]]},"abstract":"<jats:p>The identification and categorization of parasitic eggs are crucial for diagnosing parasitic infections, which pose a significant threat to global health. These methods also require specialized knowledge and can be expensive. In contrast, recent progress in artificial intelligence, particularly deep learning, has revolutionized this field by automating detection processes with high levels of accuracy and precision, reducing the need for specialized knowledge, and improving diagnostic speed. Transfer learning methods using convolutional neural networks (CNNs)-based models like AlexNet, ResNet, VGG16 and EfficientNet-B4 have yielded promising outcomes. Likewise, cutting-edge object detection models, such as the YOLO series (YOLOv5, YOLOv7, YOLOv8), Faster R-CNN, and TOOD have significantly enhanced detection efficiency. Moreover, innovative architectures like YAC-Net, which incorporate algorithmic modifications, have shown superior performance compared to traditional models like YOLO in addressing domain-specific challenges. Advanced models such as Vision Transformers, Cascade Mask R-CNN, Swin Transformers, and the DETR framework have demonstrated remarkable potential in object detection and classification tasks. DenseNet121, with its efficient feature extraction capabilities, and CoAtNet, a hybrid model leveraging the strengths of ConvNets, Vision Transformers and Ensemble learning have further enriched the field. This study examines these advancements, assesses their performance across key metrics, and explores their applicability in real-world clinical environments, offering insights into current limitations and future directions for improving parasitic egg detection and classification.<\/jats:p>","DOI":"10.3233\/faia250552","type":"book-chapter","created":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T13:20:02Z","timestamp":1758028802000},"source":"Crossref","is-referenced-by-count":0,"title":["Parasitic Egg Detection and Classification: An Overview of Recent Progress and New Challenges"],"prefix":"10.3233","author":[{"family":"D. Gnanavenkata Kumar","sequence":"first","affiliation":[{"name":"Department of CSE, AP IIIT, RGUKT, Idupulapaya, India; dg@rguktrkv.ac.in, r190063@rguktrkv.ac.in, r191089@rguktrkv.ac.in, raviua138@rguktrkv.ac.in"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arigala","family":"Adarsh","sequence":"additional","affiliation":[{"name":"Department of CSE, AP IIIT, RGUKT, Idupulapaya, India; dg@rguktrkv.ac.in, r190063@rguktrkv.ac.in, r191089@rguktrkv.ac.in, raviua138@rguktrkv.ac.in"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Reddy Palli","family":"Trisha","sequence":"additional","affiliation":[{"name":"Department of CSE, AP IIIT, RGUKT, Idupulapaya, India; dg@rguktrkv.ac.in, r190063@rguktrkv.ac.in, r191089@rguktrkv.ac.in, raviua138@rguktrkv.ac.in"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Penugonda","family":"Ravikumar","sequence":"additional","affiliation":[{"name":"Department of CSE, AP IIIT, RGUKT, Idupulapaya, India; dg@rguktrkv.ac.in, r190063@rguktrkv.ac.in, r191089@rguktrkv.ac.in, raviua138@rguktrkv.ac.in"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","New Trends in Intelligent Software Methodologies, Tools and Techniques"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA250552","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T13:20:02Z","timestamp":1758028802000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA250552"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,16]]},"ISBN":["9781643686196"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia250552","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,16]]}}}