{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T17:55:15Z","timestamp":1773856515809,"version":"3.50.1"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>Pneumonia remains a significant global health concern, especially in regions with limited medical resources, underscoring the need for accurate, efficient, and interpretable diagnostic solutions. This study proposes a dual-architecture deep learning framework combining GhostNet and MobileNetV2 for automated pneumonia detection using chest X-ray images. The model leverages GhostNet\u2019s efficient feature extraction and MobileNetV2\u2019s lightweight precision, integrated via a custom concatenation layer to enhance performance while maintaining computational efficiency. Training was conducted on a publicly available pediatric chest X-ray dataset comprising 5,872 images from the Guangzhou Women and Children\u2019s Medical Center. A patient-level split of 70% for training, 15% for validation, and 15% for testing was used, ensuring no data leakage across subsets. Although cross-validation was not applied, generalizability was assessed on an external adult dataset from Indiana University (Open-i), with the model achieving 85% test accuracy and 87% validation accuracy. On the internal test set, the proposed method attained 97.45% accuracy, 99.82% precision, 96.74% recall, and a 98.25% F1-score, outperforming established models such as ResNet50, VGG16, and EfficientNet. Training and validation loss curves showed minimal divergence, and Grad-CAM visualizations offered interpretability by highlighting salient lung regions influencing predictions. The lightweight and adaptable nature of the model makes it particularly suitable for real-world deployment in resource-constrained healthcare environments. Future work will focus on expanding the dataset, adopting k-fold cross-validation, integrating continual learning strategies, conducting subgroup and fairness analyses, and exploring explainable AI tools to further enhance clinical applicability and trust.<\/jats:p>","DOI":"10.31449\/inf.v50i10.11293","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T11:12:54Z","timestamp":1773832374000},"source":"Crossref","is-referenced-by-count":0,"title":["Automated Pneumonia Detection Using Dual-Architecture Deep Learning with GhostNet and Mo-bileNetV2"],"prefix":"10.31449","volume":"50","author":[{"given":"Amrendra","family":"Kumar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meenu","family":"Meenu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tushant","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adarsh","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,3,18]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/11293\/6630","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/11293\/6630","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T11:12:54Z","timestamp":1773832374000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/11293"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,18]]},"references-count":0,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2026,3,18]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i10.11293","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,18]]}}}