{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T17:26:13Z","timestamp":1790184373701,"version":"4.1.0"},"reference-count":17,"publisher":"Springer Science and Business Media LLC","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,12,19]]},"DOI":"10.7759\/cureus.99649","type":"journal-article","created":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T21:14:57Z","timestamp":1766178897000},"source":"Crossref","is-referenced-by-count":2,"title":["Squeeze-and-Excitation Enhanced Convolutional Neural Networks for Multi-class Pneumonia Classification on Chest Radiographs"],"prefix":"10.7759","author":[{"given":"Kian A","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haris K","family":"Choudhary","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashley","family":"Santiago","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Neelesh S","family":"Prakash","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/S1473-3099(24)00176-2","article-title":"Global, regional, and national incidence and mortality burden of non-COVID-19 lower respiratory infections and aetiologies, 1990-2021: a systematic analysis from the Global Burden of Disease Study 2021","volume":"24","author":"GBD 2021 Lower Respiratory Infections and Antimicrobial Resistance Collaborators","year":"2024","unstructured":"GBD 2021 Lower Respiratory Infections and Antimicrobial Resistance Collaborators. Global, regional, and national incidence and mortality burden of non-COVID-19 lower respiratory infections and aetiologies, 1990-2021: a systematic analysis from the Global Burden of Disease Study 2021. Lancet Infect Dis. 2024, 24:974-1002. 10.1016\/S1473-3099(24)00176-2","journal-title":"Lancet Infect Dis"},{"key":"ref2","unstructured":"Centers for Disease Control and Prevention. multiple cause of death, 1999-2023 results. (2023). Accessed: July 28, 2025: https:\/\/wonder.cdc.gov\/controller\/datarequest\/D158."},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(10)61459-6","article-title":"Viral pneumonia","volume":"377","author":"Ruuskanen O","year":"2011","unstructured":"Ruuskanen O, Lahti E, Jennings LC, Murdoch DR. Viral pneumonia. Lancet. 2011, 377:1264-75. 10.1016\/S0140-6736(10)61459-6","journal-title":"Lancet"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.crad.2004.01.011","article-title":"Inter-observer variation in the interpretation of chest radiographs for pneumonia in community-acquired lower respiratory tract infections","volume":"59","author":"Hopstaken RM","year":"2004","unstructured":"Hopstaken RM, Witbraad T, van Engelshoven JM, Dinant GJ. Inter-observer variation in the interpretation of chest radiographs for pneumonia in community-acquired lower respiratory tract infections. Clin Radiol. 2004, 59:743-52. 10.1016\/j.crad.2004.01.011","journal-title":"Clin Radiol"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.80084","article-title":"Employing squeeze-and-excitation architecture in a fine-tuned convolutional neural network for magnetic resonance imaging tumor classification","volume":"17","author":"Huang KA","year":"2025","unstructured":"Huang KA, Alkadri A, Prakash N. Employing squeeze-and-excitation architecture in a fine-tuned convolutional neural network for magnetic resonance imaging tumor classification. Cureus. 2025, 17:e80084. 10.7759\/cureus.80084","journal-title":"Cureus"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.87071","article-title":"Leveraging transfer learning and attention mechanisms for a computed tomography lung cancer classification model","volume":"17","author":"Huang KA","year":"2025","unstructured":"Huang KA, Venkitasubramony V, Prakash NS. Leveraging transfer learning and attention mechanisms for a computed tomography lung cancer classification model. Cureus. 2025, 17:e87071. 10.7759\/cureus.87071","journal-title":"Cureus"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.80872","article-title":"Evaluating the impact of attention mechanisms on a fine-tuned neural network for magnetic resonance imaging tumor classification: a comparative analysis","volume":"17","author":"Huang KA","year":"2025","unstructured":"Huang KA, Prakash N. Evaluating the impact of attention mechanisms on a fine-tuned neural network for magnetic resonance imaging tumor classification: a comparative analysis. Cureus. 2025, 17:e80872. 10.7759\/cureus.80872","journal-title":"Cureus"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.3390\/app10093233","article-title":"Transfer learning with deep convolutional neural network (CNN) for pneumonia detection using chest X\u2011ray","volume":"10","author":"Rahman T","year":"2020","unstructured":"Rahman T, Chowdhury MEH, Khandakar A, et al.. Transfer learning with deep convolutional neural network (CNN) for pneumonia detection using chest X\u2011ray. Appl Sci. 2020, 10:3233. 10.3390\/app10093233","journal-title":"Appl Sci"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s12559-020-09787-5","article-title":"Pneumonia classification using deep learning from chest x-ray images during COVID-19","volume":"16","author":"Ibrahim AU","year":"2021","unstructured":"Ibrahim AU, Ozsoz M, Serte S, Al-Turjman F, Yakoi PS. Pneumonia classification using deep learning from chest x-ray images during COVID-19. Cognit Comput. 2021, 16:1589-1601. 10.1007\/s12559-020-09787-5","journal-title":"Cognit Comput"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ASSIC60049.2024.10507933","article-title":"Deep learning techniques for identification of pneumonia: a CNN approach","author":"Das R","year":"2024","unstructured":"Das R, Nayak DSK, Rout CP, Jena L, Swarnkar T. Deep learning techniques for identification of pneumonia: a CNN approach. International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC). 2024, 1-5. 10.1109\/ASSIC60049.2024.10507933","journal-title":"International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC)"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2993291","article-title":"Deep learning COVID-19 features on CXR using limited training data sets","volume":"39","author":"Oh Y","year":"2020","unstructured":"Oh Y, Park S, Ye JC. Deep learning COVID-19 features on CXR using limited training data sets. IEEE Trans Med Imaging. 2020, 39:2688-700. 10.1109\/TMI.2020.2993291","journal-title":"IEEE Trans Med Imaging"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1002686","article-title":"Deep learning for chest radiograph diagnosis: a retrospective comparison of the CheXNeXt algorithm to practicing radiologists","volume":"15","author":"Rajpurkar P","year":"2018","unstructured":"Rajpurkar P, Irvin J, Ball RL, et al.. Deep learning for chest radiograph diagnosis: a retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med. 2018, 15:e1002686. 10.1371\/journal.pmed.1002686","journal-title":"PLoS Med"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.44130","article-title":"Convolutional neural networks (CNNs) for pneumonia classification on pediatric chest radiographs","volume":"15","author":"Saboo YS","year":"2023","unstructured":"Saboo YS, Kapse S, Prasanna P. Convolutional neural networks (CNNs) for pneumonia classification on pediatric chest radiographs. Cureus. 2023, 15:e44130. 10.7759\/cureus.44130","journal-title":"Cureus"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12051280","article-title":"Pneumonia classification from X-ray images with Inception-V3 and convolutional neural network","volume":"12","author":"Mujahid M","year":"2022","unstructured":"Mujahid M, Rustam F, \u00c1lvarez R, Luis Vidal Maz\u00f3n J, D\u00edez IT, Ashraf I. Pneumonia classification from X-ray images with Inception-V3 and convolutional neural network. Diagnostics. 2022, 12:1280. 10.3390\/diagnostics12051280","journal-title":"Diagnostics"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-99-4717-1_2","article-title":"Implementation of ResNet-50 with the skip connection principle in transfer learning models for lung disease prediction","author":"Prabakaran J","year":"2023","unstructured":"Prabakaran J, Selvaraj P. Implementation of ResNet-50 with the skip connection principle in transfer learning models for lung disease prediction. Intelligent Systems and Sustainable Computing. ICISSC 2022. Smart Innovation, Systems and Technologies. Reddy VS, Prasad VK, Wang J, Rao Dasari NM (ed): Springer, Singapore; 2023. 363:9-19. 10.1007\/978-981-99-4717-1_2"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1693\/1\/012173","article-title":"An overview of the attention mechanisms in computer vision","volume":"1693","author":"Yang X","year":"2020","unstructured":"Yang X. An overview of the attention mechanisms in computer vision. J Phys Conf Ser. 2020, 1693:012173. 10.1088\/1742-6596\/1693\/1\/012173","journal-title":"J Phys Conf Ser"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745","article-title":"Squeeze-and-excitation networks","author":"Hu J","year":"2018","unstructured":"Hu J, Shen L, Sun G. Squeeze-and-excitation networks. 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2018, 7132-41. 10.1109\/CVPR.2018.00745","journal-title":"2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition"}],"container-title":["Cureus"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cureus.com\/articles\/426795-squeeze-and-excitation-enhanced-convolutional-neural-networks-for-multi-class-pneumonia-classification-on-chest-radiographs","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T21:14:57Z","timestamp":1766178897000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cureus.com\/articles\/426795-squeeze-and-excitation-enhanced-convolutional-neural-networks-for-multi-class-pneumonia-classification-on-chest-radiographs"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,19]]},"references-count":17,"URL":"https:\/\/doi.org\/10.7759\/cureus.99649","relation":{},"ISSN":["2168-8184"],"issn-type":[{"value":"2168-8184","type":"print"}],"subject":[],"published":{"date-parts":[[2025,12,19]]}}}