{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T07:31:27Z","timestamp":1768980687558,"version":"3.49.0"},"reference-count":66,"publisher":"Public Library of Science (PLoS)","issue":"1","license":[{"start":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T00:00:00Z","timestamp":1768867200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["JP25K21814"],"award-info":[{"award-number":["JP25K21814"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>\n                    Image Quality Assessment (IQA) plays a critical role in image-based decision-making systems, especially in domains requiring high diagnostic precision. Effective feature information is a prerequisite for the high performance of machine learning methods in parasitic organism detection, and the quality of this feature information is influenced by the quality of the images. However, No-Reference IQA (NR-IQA) models have ignored microscopy-based datasets, particularly those involving parasitic organisms such as\n                    <jats:italic>Cryptosporidium spp.<\/jats:italic>\n                    and\n                    <jats:italic>Giardia spp.<\/jats:italic>\n                    , which are vital for public health inspection. In this study, PRIQA (Parasite ResNet-101 IQA), a novel deep learning-based NR-IQA model specifically trained on a small parasite image dataset was presented. Using Mean Opinion Scores (MOS) from twenty human evaluators, nine Deep Convolutional Neural Network (DCNN) architectures were benchmarked and identified ResNet-101 as the most robust feature extractor. The features were mapped to MOS using regression models and compared with ten state-of-the-art NR-IQA algorithms. Experimental results demonstrated that PRIQA consistently outperforms existing methods, indicating its suitability as a practical quality control tool for identifying unreliable or low-quality parasite microscopy images and supporting more consistent downstream detection and diagnostic workflows in automated inspection systems.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0341160","type":"journal-article","created":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T18:35:31Z","timestamp":1768934131000},"page":"e0341160","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep learning-based no-reference image quality assessment framework for Cryptosporidium spp. and Giardia spp"],"prefix":"10.1371","volume":"21","author":[{"given":"Muhammad Amirul Aiman","family":"Asri","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heshalini","family":"Rajagopal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2839-1336","authenticated-orcid":true,"given":"Norrima","family":"Mokhtar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wan Amirul","family":"Wan Mohd Mahiyiddin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yvonne Ai 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