{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T05:35:30Z","timestamp":1772602530076,"version":"3.50.1"},"reference-count":59,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T00:00:00Z","timestamp":1737936000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Pneumonia remains a significant cause of morbidity and mortality among pediatric patients worldwide. Accurate and timely diagnosis is crucial for effective treatment and improved patient outcomes. Traditionally, pneumonia diagnosis has relied on a combination of clinical evaluation and radiologists\u2019 interpretation of chest X-rays. However, this process is time-consuming and prone to inconsistencies in diagnosis. The integration of advanced technologies such as Convolutional Neural Networks (CNNs) into medical diagnostics offers a potential to enhance the accuracy and efficiency. In this study, we conduct a comprehensive evaluation of various activation functions within CNNs for pediatric pneumonia classification using a dataset of 5856 chest X-ray images. The novel Mish activation function was compared with Swish and ReLU, demonstrating superior performance in terms of accuracy, precision, recall, and F1-score in all cases. Notably, InceptionResNetV2 combined with Mish activation function achieved the highest overall performance with an accuracy of 97.61%. Although the dataset used may not fully represent the diversity of real-world clinical cases, this research provides valuable insights into the influence of activation functions on CNN performance in medical image analysis, laying a foundation for future automated pneumonia diagnostic systems.<\/jats:p>","DOI":"10.3390\/bdcc9020025","type":"journal-article","created":{"date-parts":[[2025,1,27]],"date-time":"2025-01-27T11:38:49Z","timestamp":1737977929000},"page":"25","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Optimizing Convolutional Neural Network Architectures with Optimal Activation Functions for Pediatric Pneumonia Diagnosis Using Chest X-Rays"],"prefix":"10.3390","volume":"9","author":[{"given":"Petra","family":"Rado\u010daj","sequence":"first","affiliation":[{"name":"Layer d.o.o., Vukovarska cesta 31, 31000 Osijek, Croatia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7151-7862","authenticated-orcid":false,"given":"Dorijan","family":"Rado\u010daj","sequence":"additional","affiliation":[{"name":"Faculty of Agrobiotechnical Sciences Osijek, Josip Juraj Strossmayer University of Osijek, Vladimira Preloga 1, 31000 Osijek, Croatia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7469-6018","authenticated-orcid":false,"given":"Goran","family":"Martinovi\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, Computer Science and Information Technology, Josip Juraj Strossmayer University of Osijek, Kneza Trpimira 2B, 31000 Osijek, Croatia"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1002\/ppul.23030","article-title":"The Global Burden of Respiratory Disease-Impact on Child Health: The Global Burden of Respiratory Disease","volume":"49","author":"Zar","year":"2014","journal-title":"Pediatr. 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