{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T02:18:42Z","timestamp":1773368322935,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T00:00:00Z","timestamp":1773273600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Walailak University Graduate Scholarships","award":["28\/2023"],"award-info":[{"award-number":["28\/2023"]}]},{"name":"Walailak University Graduate Research Fund","award":["CGS-RF-2025\/03"],"award-info":[{"award-number":["CGS-RF-2025\/03"]}]},{"name":"National Research Council of Thailand (NRCT) and Walailak University","award":["N42A650376"],"award-info":[{"award-number":["N42A650376"]}]},{"name":"School of Allied Health Sciences, Walailak University, under the Research Cluster Accelerator scheme"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Manual microscopy for malaria diagnosis is labor-intensive and prone to inter-observer variability. This study presents an automated binary classification approach for detecting malaria ring-form infections in thin blood smear single-cell images using a parallel neural network framework. Utilizing a balanced Kaggle dataset of 27,558 erythrocyte crops, images were standardized to 128 \u00d7 128 pixels and subjected to on-the-fly augmentation. The proposed architecture employs a dual-branch fusion strategy, integrating a convolutional neural network for local morphological feature extraction with a multi-head self-attention branch to capture global spatial relationships. Performance was rigorously evaluated using 10-fold stratified cross-validation and an independent 10% hold-out test set. Results demonstrated high-level discrimination, with all models achieving an ROC\u2013AUC of approximately 0.99. The primary model (Model#1) attained a peak mean accuracy of 0.9567 during cross-validation and 0.97 accuracy (macro F1-score: 0.97) on the independent test set. In contrast, increasing architectural complexity in Model#3 led to a performance decline (0.95 accuracy) due to higher false-positive rates. These findings suggest that moderate-capacity feature fusion, combining convolutional descriptors with attention-based aggregation, provides a robust and generalizable solution for automated malaria screening without the risks associated with over-parameterization. Despite a strong performance, immediate clinical use remains limited because the model was developed on pre-segmented single-cell images, and external validation is still required before routine implementation.<\/jats:p>","DOI":"10.3390\/jimaging12030127","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T13:12:24Z","timestamp":1773321144000},"page":"127","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated Malaria Ring Form Classification in Blood Smear Images Using Ensemble Parallel Neural Networks"],"prefix":"10.3390","volume":"12","author":[{"given":"Pongphan","family":"Pongpanitanont","sequence":"first","affiliation":[{"name":"Health Sciences (International Program), College of Graduate Studies, Walailak University, Nakhon Si Thammarat 80160, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naparat","family":"Suttidate","sequence":"additional","affiliation":[{"name":"Health Sciences (International Program), College of Graduate Studies, Walailak University, Nakhon Si Thammarat 80160, Thailand"},{"name":"Akkhraratchakumari Veterinary College, Walailak University, Nakhon Si Thammarat 80160, Thailand"},{"name":"Centre for One Health, Walailak University, Nakhon Si Thammarat 80160, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1325-5489","authenticated-orcid":false,"given":"Manit","family":"Nuinoon","sequence":"additional","affiliation":[{"name":"Department of Medical Technology, School of Allied Health Sciences, Walailak University, Nakhon Si Thammarat 80160, Thailand"},{"name":"Hematology and Transfusion Science Research Center, Walailak University, Nakhon Si Thammarat 80160, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Natthida","family":"Khampeeramao","sequence":"additional","affiliation":[{"name":"Health Sciences (International Program), College of Graduate Studies, Walailak University, Nakhon Si Thammarat 80160, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sakhone","family":"Laymanivong","sequence":"additional","affiliation":[{"name":"Centre of Malariology, Parasitology and Entomology, Ministry of Health, Vientiane P.O. Box 0100, Laos"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Penchom","family":"Janwan","sequence":"additional","affiliation":[{"name":"Health Sciences (International Program), College of Graduate Studies, Walailak University, Nakhon Si Thammarat 80160, Thailand"},{"name":"Department of Medical Technology, School of Allied Health Sciences, Walailak University, Nakhon Si Thammarat 80160, Thailand"},{"name":"Hematology and Transfusion Science Research Center, Walailak University, Nakhon Si Thammarat 80160, Thailand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,12]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2025, December 30). World Malaria Report 2024. 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