{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T19:43:22Z","timestamp":1773344602153,"version":"3.50.1"},"reference-count":30,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T00:00:00Z","timestamp":1764374400000},"content-version":"vor","delay-in-days":332,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["BioMed Research International"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>\n                    Soil\u2010transmitted helminths (STHs) and schistosomiasis are widespread parasitic diseases in tropical regions, particularly in Africa, with substantial health and socioeconomic burdens. Early diagnosis and treatment are critical for mitigating these impacts. Conventional microscopy\u2010based diagnosis was time\u2010consuming and labor\u2010intensive, posing challenges in resource\u2010limited settings such as Ethiopia. This study developed an innovative system that combined machine learning (ML) and deep learning to analyze microscope images of parasite eggs, improving diagnostic speed and accuracy compared to traditional CNN\u2010only approaches. We compared a hybrid CNN\u2013ML approach with standalone deep learning models and vision transformers (ViTs) for classifying five categories:\n                    <jats:italic>Ascaris<\/jats:italic>\n                    , hookworm, schistosomiasis,\n                    <jats:italic>Trichuris<\/jats:italic>\n                    , and negative samples. The dataset comprised 1490 images from the Ethiopian Public Health Institute, processed with resizing, normalization, and augmentation. CNN architectures (VGG16, ResNet50, DenseNet121, MobileNetV2, and EfficientNetB0) and ViT served as feature extractors, with ML classifiers (SVM, XGBoost, KNN, RF, and DT) performing the predictions. The hybrid CNN\u2013ML model outperformed standalone models, with VGG16\u2010SVM and VGG16\u2010XGBoost achieving the highest test accuracy of 99.31% and 99.35%, respectively. In contrast, standalone CNNs showed lower accuracy (VGG16: 79.98%; DenseNet121: 84.12%). Negative samples were classified with high accuracy across models, while parasite classes exhibited varying performance depending on the architecture. This system enhances diagnostic utility in low\u2010resource settings by enabling real\u2010time analysis. However, limitations include a small, long\u2010stored dataset with limited diversity and potential degradation, which may affect model generalizability.\n                  <\/jats:p>","DOI":"10.1155\/bmri\/8074581","type":"journal-article","created":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T12:25:49Z","timestamp":1764419149000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Constructing a Predictive Model for STH and Schistosomiasis Classification From Microscopic Images"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9609-0480","authenticated-orcid":false,"given":"Etefa","family":"Belachew","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4684-755X","authenticated-orcid":false,"given":"Kris","family":"Calpotura","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6203-4145","authenticated-orcid":false,"given":"Abrham","family":"Adamu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Berhanu","family":"Getachew","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,11,29]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pntd.0007201"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.3390\/microorganisms10071353"},{"key":"e_1_2_11_3_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pntd.0012282"},{"key":"e_1_2_11_4_2","doi-asserted-by":"publisher","DOI":"10.4269\/ajtmh.19-0704"},{"key":"e_1_2_11_5_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13071-020-04317-6"},{"key":"e_1_2_11_6_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pntd.0009254"},{"key":"e_1_2_11_7_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pntd.0009032"},{"key":"e_1_2_11_8_2","volume-title":"Computer-Assisted Microscopy. 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